Supervised Learning : Characterizing Life Expectancy Drivers Across Countries Using Model-Agnostic Interpretation Methods for Black-Box Models
John Pauline Pineda
July 15, 2023
- 1.
Table of Contents
- 1.1 Introduction
- 1.2 Methodology
- 1.3 Results
- 1.4 Summary
- 2. References
1. Table of Contents
Description
1.1 Introduction
1.1.1 Study Objectives
1.1.2 Outcome
1.1.3 Predictors
1.2 Methodology
1.2.1 Model Formulation
1.2.2 Model Evaluation
1.2.3 Model Post-Hoc Analysis
1.3 Results
1.3.1 Data Preparation
Code Chunk | Output
##################################
# Loading R libraries
##################################
library(DALEX)
library(caret)
library(randomForest)
library(e1071)
library(gbm)
library(skimr)
library(corrplot)
library(lares)
library(dplyr)
library(minerva)
library(CORElearn)
library(patchwork)
##################################
# Loading source and
# formulating the analysis set
##################################
LED <- read.csv("Life_Expectancy_Data.csv",
na.strings=c("NA","NaN"," ",""),
stringsAsFactors = FALSE)
LED <- as.data.frame(LED)
##################################
# Performing a general exploration of the data set
##################################
dim(LED)## [1] 396 22str(LED)## 'data.frame': 396 obs. of 22 variables:
## $ COUNTRY: chr "Afghanistan" "Albania" "Algeria" "Angola" ...
## $ YEAR : int 2019 2019 2019 2019 2019 2019 2019 2019 2019 2019 ...
## $ GENDER : chr "Female" "Female" "Female" "Female" ...
## $ LIFEXP : num 66.4 80.2 78.1 64 78.1 ...
## $ UNEMPR : num 14.06 11.32 18.63 7.84 8.26 ...
## $ INFMOR : num 42.9 7.7 18.6 44.5 5.1 ...
## $ GDP : num 1.88e+10 1.54e+10 1.72e+11 8.94e+10 1.69e+09 ...
## $ GNI : num 1.91e+10 1.52e+10 1.68e+11 8.19e+10 1.58e+09 ...
## $ CLTECH : num 36 80.7 99.3 49.6 100 ...
## $ PERCAP : num 494 5396 3990 2810 17377 ...
## $ RTIMOR : num 15.9 11.7 20.9 26.1 0 ...
## $ TUBINC : num 189 16 61 351 0 29 26 2.2 6.9 6 ...
## $ DPTIMM : num 66 99 91 57 95 ...
## $ HEPIMM : num 66 99 91 53 99 ...
## $ MEAIMM : num 64 95 80 51 93 ...
## $ HOSBED : num 0.432 3.052 1.8 0.8 2.581 ...
## $ SANSER : num 49 99.2 86.1 51.4 85.5 ...
## $ TUBTRT : num 91 88 86 69 72.3 ...
## $ URBPOP : num 25.8 61.2 73.2 66.2 24.5 ...
## $ RURPOP : num 74.2 38.8 26.8 33.8 75.5 ...
## $ NCOMOR : num 36.2 6 12.8 19.4 17.6 ...
## $ SUIRAT : num 3.6 2.7 1.8 2.3 0.8 ...summary(LED)## COUNTRY YEAR GENDER LIFEXP
## Length:396 Min. :2019 Length:396 Min. :51.20
## Class :character 1st Qu.:2019 Class :character 1st Qu.:67.62
## Mode :character Median :2019 Mode :character Median :74.38
## Mean :2019 Mean :73.10
## 3rd Qu.:2019 3rd Qu.:79.31
## Max. :2019 Max. :88.10
## UNEMPR INFMOR GDP GNI
## Min. : 0.071 Min. : 1.40 Min. :1.884e+08 Min. :3.754e+08
## 1st Qu.: 3.575 1st Qu.: 5.90 1st Qu.:1.131e+10 1st Qu.:1.114e+10
## Median : 5.647 Median :15.05 Median :3.916e+10 Median :4.007e+10
## Mean : 7.749 Mean :21.46 Mean :5.762e+11 Mean :5.972e+11
## 3rd Qu.: 9.839 3rd Qu.:30.38 3rd Qu.:2.500e+11 3rd Qu.:2.460e+11
## Max. :41.153 Max. :88.80 Max. :2.320e+13 Max. :2.340e+13
## CLTECH PERCAP RTIMOR TUBINC
## Min. : 0.00 Min. : 228.2 Min. : 0.00 Min. : 0
## 1st Qu.: 33.50 1st Qu.: 2229.9 1st Qu.: 8.20 1st Qu.: 12
## Median : 80.10 Median : 6617.1 Median :15.95 Median : 46
## Mean : 65.83 Mean : 16917.8 Mean :16.98 Mean :103
## 3rd Qu.:100.00 3rd Qu.: 19575.8 3rd Qu.:23.90 3rd Qu.:140
## Max. :100.00 Max. :175813.9 Max. :64.60 Max. :654
## DPTIMM HEPIMM MEAIMM HOSBED
## Min. :35.00 Min. :35.00 Min. :37.00 Min. : 0.200
## 1st Qu.:85.69 1st Qu.:81.31 1st Qu.:84.85 1st Qu.: 1.300
## Median :92.00 Median :91.00 Median :92.00 Median : 2.572
## Mean :87.90 Mean :86.65 Mean :87.22 Mean : 2.987
## 3rd Qu.:97.00 3rd Qu.:96.00 3rd Qu.:96.00 3rd Qu.: 3.746
## Max. :99.00 Max. :99.00 Max. :99.00 Max. :13.710
## SANSER TUBTRT URBPOP RURPOP
## Min. : 8.632 Min. : 0.00 Min. : 13.25 Min. : 0.00
## 1st Qu.: 63.898 1st Qu.: 73.00 1st Qu.: 41.61 1st Qu.:21.90
## Median : 91.239 Median : 82.00 Median : 58.90 Median :41.10
## Mean : 77.606 Mean : 77.66 Mean : 59.21 Mean :40.79
## 3rd Qu.: 98.648 3rd Qu.: 88.00 3rd Qu.: 78.10 3rd Qu.:58.39
## Max. :100.000 Max. :100.00 Max. :100.00 Max. :86.75
## NCOMOR SUIRAT
## Min. : 4.40 Min. : 0.00
## 1st Qu.:13.60 1st Qu.: 3.30
## Median :19.85 Median : 6.95
## Mean :19.99 Mean : 9.34
## 3rd Qu.:24.02 3rd Qu.:11.22
## Max. :58.40 Max. :63.00##################################
# Transforming to appropriate data types
##################################
LED$YEAR <- factor(LED$YEAR,
levels = c("2019"))
LED$GENDER <- factor(LED$GENDER,
levels = c("Male","Female"))
##################################
# Reducing the range of values
# for certain numeric predictors
##################################
LED$GDP <- LED$GDP/1000000000
LED$GNI <- LED$GNI/1000000000
LED$PERCAP <- LED$PERCAP/1000
##################################
# Formulating a data type assessment summary
##################################
PDA <- LED
(PDA.Summary <- data.frame(
Column.Index=c(1:length(names(PDA))),
Column.Name= names(PDA),
Column.Type=sapply(PDA, function(x) class(x)),
row.names=NULL)
)## Column.Index Column.Name Column.Type
## 1 1 COUNTRY character
## 2 2 YEAR factor
## 3 3 GENDER factor
## 4 4 LIFEXP numeric
## 5 5 UNEMPR numeric
## 6 6 INFMOR numeric
## 7 7 GDP numeric
## 8 8 GNI numeric
## 9 9 CLTECH numeric
## 10 10 PERCAP numeric
## 11 11 RTIMOR numeric
## 12 12 TUBINC numeric
## 13 13 DPTIMM numeric
## 14 14 HEPIMM numeric
## 15 15 MEAIMM numeric
## 16 16 HOSBED numeric
## 17 17 SANSER numeric
## 18 18 TUBTRT numeric
## 19 19 URBPOP numeric
## 20 20 RURPOP numeric
## 21 21 NCOMOR numeric
## 22 22 SUIRAT numeric1.3.2 Data Quality Assessment
Code Chunk | Output
##################################
# Loading dataset
##################################
DQA <- LED
##################################
# Formulating an overall data quality assessment summary
##################################
(DQA.Summary <- data.frame(
Column.Index=c(1:length(names(DQA))),
Column.Name= names(DQA),
Column.Type=sapply(DQA, function(x) class(x)),
Row.Count=sapply(DQA, function(x) nrow(DQA)),
NA.Count=sapply(DQA,function(x)sum(is.na(x))),
Fill.Rate=sapply(DQA,function(x)format(round((sum(!is.na(x))/nrow(DQA)),3),nsmall=3)),
row.names=NULL)
)## Column.Index Column.Name Column.Type Row.Count NA.Count Fill.Rate
## 1 1 COUNTRY character 396 0 1.000
## 2 2 YEAR factor 396 0 1.000
## 3 3 GENDER factor 396 0 1.000
## 4 4 LIFEXP numeric 396 0 1.000
## 5 5 UNEMPR numeric 396 0 1.000
## 6 6 INFMOR numeric 396 0 1.000
## 7 7 GDP numeric 396 0 1.000
## 8 8 GNI numeric 396 0 1.000
## 9 9 CLTECH numeric 396 0 1.000
## 10 10 PERCAP numeric 396 0 1.000
## 11 11 RTIMOR numeric 396 0 1.000
## 12 12 TUBINC numeric 396 0 1.000
## 13 13 DPTIMM numeric 396 0 1.000
## 14 14 HEPIMM numeric 396 0 1.000
## 15 15 MEAIMM numeric 396 0 1.000
## 16 16 HOSBED numeric 396 0 1.000
## 17 17 SANSER numeric 396 0 1.000
## 18 18 TUBTRT numeric 396 0 1.000
## 19 19 URBPOP numeric 396 0 1.000
## 20 20 RURPOP numeric 396 0 1.000
## 21 21 NCOMOR numeric 396 0 1.000
## 22 22 SUIRAT numeric 396 0 1.000##################################
# Listing all Predictors
##################################
DQA.Predictors <- DQA[,!names(DQA) %in% c("COUNTRY","YEAR","LIFEXP")]
##################################
# Listing all numeric Predictors
##################################
DQA.Predictors.Numeric <- DQA.Predictors[,sapply(DQA.Predictors, is.numeric), drop = FALSE]
if (length(names(DQA.Predictors.Numeric))>0) {
print(paste0("There is (are) ",
(length(names(DQA.Predictors.Numeric))),
" numeric descriptor variable(s)."))
} else {
print("There are no numeric descriptor variables.")
}## [1] "There is (are) 18 numeric descriptor variable(s)."##################################
# Listing all factor Predictors
##################################
DQA.Predictors.Factor <- DQA.Predictors[,sapply(DQA.Predictors, is.factor), drop = FALSE]
if (length(names(DQA.Predictors.Factor))>0) {
print(paste0("There is (are) ",
(length(names(DQA.Predictors.Factor))),
" factor descriptor variable(s)."))
} else {
print("There are no factor descriptor variables.")
}## [1] "There is (are) 1 factor descriptor variable(s)."##################################
# Formulating a data quality assessment summary for factor Predictors
##################################
if (length(names(DQA.Predictors.Factor))>0) {
##################################
# Formulating a function to determine the first mode
##################################
FirstModes <- function(x) {
ux <- unique(na.omit(x))
tab <- tabulate(match(x, ux))
ux[tab == max(tab)]
}
##################################
# Formulating a function to determine the second mode
##################################
SecondModes <- function(x) {
ux <- unique(na.omit(x))
tab <- tabulate(match(x, ux))
fm = ux[tab == max(tab)]
sm = x[!(x %in% fm)]
usm <- unique(sm)
tabsm <- tabulate(match(sm, usm))
ifelse(is.na(usm[tabsm == max(tabsm)])==TRUE,
return("x"),
return(usm[tabsm == max(tabsm)]))
}
(DQA.Predictors.Factor.Summary <- data.frame(
Column.Name= names(DQA.Predictors.Factor),
Column.Type=sapply(DQA.Predictors.Factor, function(x) class(x)),
Unique.Count=sapply(DQA.Predictors.Factor, function(x) length(unique(x))),
First.Mode.Value=sapply(DQA.Predictors.Factor, function(x) as.character(FirstModes(x)[1])),
Second.Mode.Value=sapply(DQA.Predictors.Factor, function(x) as.character(SecondModes(x)[1])),
First.Mode.Count=sapply(DQA.Predictors.Factor, function(x) sum(na.omit(x) == FirstModes(x)[1])),
Second.Mode.Count=sapply(DQA.Predictors.Factor, function(x) sum(na.omit(x) == SecondModes(x)[1])),
Unique.Count.Ratio=sapply(DQA.Predictors.Factor, function(x) format(round((length(unique(x))/nrow(DQA.Predictors.Factor)),3), nsmall=3)),
First.Second.Mode.Ratio=sapply(DQA.Predictors.Factor, function(x) format(round((sum(na.omit(x) == FirstModes(x)[1])/sum(na.omit(x) == SecondModes(x)[1])),3), nsmall=3)),
row.names=NULL)
)
}## Column.Name Column.Type Unique.Count First.Mode.Value Second.Mode.Value
## 1 GENDER factor 2 Female x
## First.Mode.Count Second.Mode.Count Unique.Count.Ratio First.Second.Mode.Ratio
## 1 198 0 0.005 Inf##################################
# Formulating a data quality assessment summary for numeric Predictors
##################################
if (length(names(DQA.Predictors.Numeric))>0) {
##################################
# Formulating a function to determine the first mode
##################################
FirstModes <- function(x) {
ux <- unique(na.omit(x))
tab <- tabulate(match(x, ux))
ux[tab == max(tab)]
}
##################################
# Formulating a function to determine the second mode
##################################
SecondModes <- function(x) {
ux <- unique(na.omit(x))
tab <- tabulate(match(x, ux))
fm = ux[tab == max(tab)]
sm = na.omit(x)[!(na.omit(x) %in% fm)]
usm <- unique(sm)
tabsm <- tabulate(match(sm, usm))
ifelse(is.na(usm[tabsm == max(tabsm)])==TRUE,
return(0.00001),
return(usm[tabsm == max(tabsm)]))
}
(DQA.Predictors.Numeric.Summary <- data.frame(
Column.Name= names(DQA.Predictors.Numeric),
Column.Type=sapply(DQA.Predictors.Numeric, function(x) class(x)),
Unique.Count=sapply(DQA.Predictors.Numeric, function(x) length(unique(x))),
Unique.Count.Ratio=sapply(DQA.Predictors.Numeric, function(x) format(round((length(unique(x))/nrow(DQA.Predictors.Numeric)),3), nsmall=3)),
First.Mode.Value=sapply(DQA.Predictors.Numeric, function(x) format(round((FirstModes(x)[1]),3),nsmall=3)),
Second.Mode.Value=sapply(DQA.Predictors.Numeric, function(x) format(round((SecondModes(x)[1]),3),nsmall=3)),
First.Mode.Count=sapply(DQA.Predictors.Numeric, function(x) sum(na.omit(x) == FirstModes(x)[1])),
Second.Mode.Count=sapply(DQA.Predictors.Numeric, function(x) sum(na.omit(x) == SecondModes(x)[1])),
First.Second.Mode.Ratio=sapply(DQA.Predictors.Numeric, function(x) format(round((sum(na.omit(x) == FirstModes(x)[1])/sum(na.omit(x) == SecondModes(x)[1])),3), nsmall=3)),
Minimum=sapply(DQA.Predictors.Numeric, function(x) format(round(min(x,na.rm = TRUE),3), nsmall=3)),
Mean=sapply(DQA.Predictors.Numeric, function(x) format(round(mean(x,na.rm = TRUE),3), nsmall=3)),
Median=sapply(DQA.Predictors.Numeric, function(x) format(round(median(x,na.rm = TRUE),3), nsmall=3)),
Maximum=sapply(DQA.Predictors.Numeric, function(x) format(round(max(x,na.rm = TRUE),3), nsmall=3)),
Skewness=sapply(DQA.Predictors.Numeric, function(x) format(round(skewness(x,na.rm = TRUE),3), nsmall=3)),
Kurtosis=sapply(DQA.Predictors.Numeric, function(x) format(round(kurtosis(x,na.rm = TRUE),3), nsmall=3)),
Percentile25th=sapply(DQA.Predictors.Numeric, function(x) format(round(quantile(x,probs=0.25,na.rm = TRUE),3), nsmall=3)),
Percentile75th=sapply(DQA.Predictors.Numeric, function(x) format(round(quantile(x,probs=0.75,na.rm = TRUE),3), nsmall=3)),
row.names=NULL)
)
}## Column.Name Column.Type Unique.Count Unique.Count.Ratio First.Mode.Value
## 1 UNEMPR numeric 372 0.939 8.256
## 2 INFMOR numeric 245 0.619 30.235
## 3 GDP numeric 193 0.487 1390.000
## 4 GNI numeric 192 0.485 2040.000
## 5 CLTECH numeric 112 0.283 100.000
## 6 PERCAP numeric 197 0.497 12.669
## 7 RTIMOR numeric 142 0.359 18.229
## 8 TUBINC numeric 146 0.369 136.043
## 9 DPTIMM numeric 46 0.116 99.000
## 10 HEPIMM numeric 46 0.116 81.308
## 11 MEAIMM numeric 48 0.121 99.000
## 12 HOSBED numeric 174 0.439 2.986
## 13 SANSER numeric 187 0.472 100.000
## 14 TUBTRT numeric 59 0.149 84.000
## 15 URBPOP numeric 192 0.485 100.000
## 16 RURPOP numeric 192 0.485 0.000
## 17 NCOMOR numeric 216 0.545 22.100
## 18 SUIRAT numeric 177 0.447 10.619
## Second.Mode.Value First.Mode.Count Second.Mode.Count First.Second.Mode.Ratio
## 1 3.924 22 2 11.000
## 2 2.100 28 7 4.000
## 3 18.799 4 2 2.000
## 4 316.000 8 4 2.000
## 5 60.593 110 34 3.235
## 6 0.494 4 2 2.000
## 7 26.800 28 6 4.667
## 8 35.000 12 10 1.200
## 9 85.685 44 30 1.467
## 10 99.000 40 38 1.053
## 11 84.855 48 30 1.600
## 12 0.400 34 8 4.250
## 13 49.006 24 2 12.000
## 14 83.000 22 20 1.100
## 15 55.985 10 4 2.500
## 16 44.015 10 4 2.500
## 17 6.800 30 5 6.000
## 18 7.600 30 8 3.750
## Minimum Mean Median Maximum Skewness Kurtosis Percentile25th
## 1 0.071 7.749 5.648 41.153 1.758 3.711 3.575
## 2 1.400 21.464 15.050 88.800 1.090 0.580 5.900
## 3 0.188 576.200 39.162 23200.000 7.506 59.379 11.315
## 4 0.375 597.197 40.068 23400.000 7.467 59.073 11.141
## 5 0.000 65.833 80.100 100.000 -0.630 -1.132 33.500
## 6 0.228 16.918 6.617 175.814 2.746 10.405 2.230
## 7 0.000 16.977 15.950 64.600 0.747 1.046 8.200
## 8 0.000 102.983 46.000 654.000 1.870 3.203 12.000
## 9 35.000 87.904 92.000 99.000 -1.864 3.470 85.685
## 10 35.000 86.654 91.000 99.000 -1.602 2.510 81.308
## 11 37.000 87.221 92.000 99.000 -1.695 2.607 84.855
## 12 0.200 2.987 2.572 13.710 1.700 3.893 1.300
## 13 8.632 77.606 91.239 100.000 -1.129 -0.139 63.898
## 14 0.000 77.662 82.000 100.000 -2.197 5.606 73.000
## 15 13.250 59.211 58.900 100.000 -0.141 -0.994 41.612
## 16 0.000 40.789 41.100 86.750 0.141 -0.994 21.901
## 17 4.400 19.986 19.850 58.400 0.870 1.558 13.600
## 18 0.000 9.340 6.950 63.000 2.325 7.134 3.300
## Percentile75th
## 1 9.840
## 2 30.376
## 3 250.000
## 4 246.000
## 5 100.000
## 6 19.576
## 7 23.900
## 8 140.000
## 9 97.000
## 10 96.000
## 11 96.000
## 12 3.746
## 13 98.648
## 14 88.000
## 15 78.099
## 16 58.388
## 17 24.025
## 18 11.225##################################
# Identifying potential data quality issues
##################################
##################################
# Checking for missing observations
##################################
if ((nrow(DQA.Summary[DQA.Summary$NA.Count>0,]))>0){
print(paste0("Missing observations noted for ",
(nrow(DQA.Summary[DQA.Summary$NA.Count>0,])),
" variable(s) with NA.Count>0 and Fill.Rate<1.0."))
DQA.Summary[DQA.Summary$NA.Count>0,]
} else {
print("No missing observations noted.")
}## [1] "No missing observations noted."##################################
# Checking for zero or near-zero variance Predictors
##################################
if (length(names(DQA.Predictors.Factor))==0) {
print("No factor Predictors noted.")
} else if (nrow(DQA.Predictors.Factor.Summary[as.numeric(as.character(DQA.Predictors.Factor.Summary$First.Second.Mode.Ratio))>5,])>0){
print(paste0("Low variance observed for ",
(nrow(DQA.Predictors.Factor.Summary[as.numeric(as.character(DQA.Predictors.Factor.Summary$First.Second.Mode.Ratio))>5,])),
" factor variable(s) with First.Second.Mode.Ratio>5."))
DQA.Predictors.Factor.Summary[as.numeric(as.character(DQA.Predictors.Factor.Summary$First.Second.Mode.Ratio))>5,]
} else {
print("No low variance factor Predictors due to high first-second mode ratio noted.")
}## [1] "Low variance observed for 1 factor variable(s) with First.Second.Mode.Ratio>5."## Column.Name Column.Type Unique.Count First.Mode.Value Second.Mode.Value
## 1 GENDER factor 2 Female x
## First.Mode.Count Second.Mode.Count Unique.Count.Ratio First.Second.Mode.Ratio
## 1 198 0 0.005 Infif (length(names(DQA.Predictors.Numeric))==0) {
print("No numeric Predictors noted.")
} else if (nrow(DQA.Predictors.Numeric.Summary[as.numeric(as.character(DQA.Predictors.Numeric.Summary$First.Second.Mode.Ratio))>5,])>0){
print(paste0("Low variance observed for ",
(nrow(DQA.Predictors.Numeric.Summary[as.numeric(as.character(DQA.Predictors.Numeric.Summary$First.Second.Mode.Ratio))>5,])),
" numeric variable(s) with First.Second.Mode.Ratio>5."))
DQA.Predictors.Numeric.Summary[as.numeric(as.character(DQA.Predictors.Numeric.Summary$First.Second.Mode.Ratio))>5,]
} else {
print("No low variance numeric Predictors due to high first-second mode ratio noted.")
}## [1] "Low variance observed for 3 numeric variable(s) with First.Second.Mode.Ratio>5."## Column.Name Column.Type Unique.Count Unique.Count.Ratio First.Mode.Value
## 1 UNEMPR numeric 372 0.939 8.256
## 13 SANSER numeric 187 0.472 100.000
## 17 NCOMOR numeric 216 0.545 22.100
## Second.Mode.Value First.Mode.Count Second.Mode.Count First.Second.Mode.Ratio
## 1 3.924 22 2 11.000
## 13 49.006 24 2 12.000
## 17 6.800 30 5 6.000
## Minimum Mean Median Maximum Skewness Kurtosis Percentile25th
## 1 0.071 7.749 5.648 41.153 1.758 3.711 3.575
## 13 8.632 77.606 91.239 100.000 -1.129 -0.139 63.898
## 17 4.400 19.986 19.850 58.400 0.870 1.558 13.600
## Percentile75th
## 1 9.840
## 13 98.648
## 17 24.025if (length(names(DQA.Predictors.Numeric))==0) {
print("No numeric Predictors noted.")
} else if (nrow(DQA.Predictors.Numeric.Summary[as.numeric(as.character(DQA.Predictors.Numeric.Summary$Unique.Count.Ratio))<0.01,])>0){
print(paste0("Low variance observed for ",
(nrow(DQA.Predictors.Numeric.Summary[as.numeric(as.character(DQA.Predictors.Numeric.Summary$Unique.Count.Ratio))<0.01,])),
" numeric variable(s) with Unique.Count.Ratio<0.01."))
DQA.Predictors.Numeric.Summary[as.numeric(as.character(DQA.Predictors.Numeric.Summary$Unique.Count.Ratio))<0.01,]
} else {
print("No low variance numeric Predictors due to low unique count ratio noted.")
}## [1] "No low variance numeric Predictors due to low unique count ratio noted."##################################
# Checking for skewed Predictors
##################################
if (length(names(DQA.Predictors.Numeric))==0) {
print("No numeric Predictors noted.")
} else if (nrow(DQA.Predictors.Numeric.Summary[as.numeric(as.character(DQA.Predictors.Numeric.Summary$Skewness))>3 |
as.numeric(as.character(DQA.Predictors.Numeric.Summary$Skewness))<(-3),])>0){
print(paste0("High skewness observed for ",
(nrow(DQA.Predictors.Numeric.Summary[as.numeric(as.character(DQA.Predictors.Numeric.Summary$Skewness))>3 |
as.numeric(as.character(DQA.Predictors.Numeric.Summary$Skewness))<(-3),])),
" numeric variable(s) with Skewness>3 or Skewness<(-3)."))
DQA.Predictors.Numeric.Summary[as.numeric(as.character(DQA.Predictors.Numeric.Summary$Skewness))>3 |
as.numeric(as.character(DQA.Predictors.Numeric.Summary$Skewness))<(-3),]
} else {
print("No skewed numeric Predictors noted.")
}## [1] "High skewness observed for 2 numeric variable(s) with Skewness>3 or Skewness<(-3)."## Column.Name Column.Type Unique.Count Unique.Count.Ratio First.Mode.Value
## 3 GDP numeric 193 0.487 1390.000
## 4 GNI numeric 192 0.485 2040.000
## Second.Mode.Value First.Mode.Count Second.Mode.Count First.Second.Mode.Ratio
## 3 18.799 4 2 2.000
## 4 316.000 8 4 2.000
## Minimum Mean Median Maximum Skewness Kurtosis Percentile25th
## 3 0.188 576.200 39.162 23200.000 7.506 59.379 11.315
## 4 0.375 597.197 40.068 23400.000 7.467 59.073 11.141
## Percentile75th
## 3 250.000
## 4 246.0001.3.3 Data Preprocessing
1.3.3.1 Outlier Treatment
Code Chunk | Output
##################################
# Loading dataset
##################################
DPA <- LED
##################################
# Gathering descriptive statistics
##################################
(DPA_Skimmed <- skim(DPA))| Name | DPA |
| Number of rows | 396 |
| Number of columns | 22 |
| _______________________ | |
| Column type frequency: | |
| character | 1 |
| factor | 2 |
| numeric | 19 |
| ________________________ | |
| Group variables | None |
Variable type: character
| skim_variable | n_missing | complete_rate | min | max | empty | n_unique | whitespace |
|---|---|---|---|---|---|---|---|
| COUNTRY | 0 | 1 | 4 | 30 | 0 | 198 | 0 |
Variable type: factor
| skim_variable | n_missing | complete_rate | ordered | n_unique | top_counts |
|---|---|---|---|---|---|
| YEAR | 0 | 1 | FALSE | 1 | 201: 396 |
| GENDER | 0 | 1 | FALSE | 2 | Mal: 198, Fem: 198 |
Variable type: numeric
| skim_variable | n_missing | complete_rate | mean | sd | p0 | p25 | p50 | p75 | p100 | hist |
|---|---|---|---|---|---|---|---|---|---|---|
| LIFEXP | 0 | 1 | 73.10 | 7.82 | 51.20 | 67.62 | 74.38 | 79.31 | 88.10 | ▁▃▆▇▃ |
| UNEMPR | 0 | 1 | 7.75 | 6.34 | 0.07 | 3.58 | 5.65 | 9.84 | 41.15 | ▇▂▁▁▁ |
| INFMOR | 0 | 1 | 21.46 | 18.66 | 1.40 | 5.90 | 15.05 | 30.38 | 88.80 | ▇▃▂▁▁ |
| GDP | 0 | 1 | 576.20 | 2504.77 | 0.19 | 11.31 | 39.16 | 250.00 | 23200.00 | ▇▁▁▁▁ |
| GNI | 0 | 1 | 597.20 | 2530.16 | 0.38 | 11.14 | 40.07 | 246.00 | 23400.00 | ▇▁▁▁▁ |
| CLTECH | 0 | 1 | 65.83 | 36.32 | 0.00 | 33.50 | 80.10 | 100.00 | 100.00 | ▃▁▁▂▇ |
| PERCAP | 0 | 1 | 16.92 | 24.49 | 0.23 | 2.23 | 6.62 | 19.58 | 175.81 | ▇▁▁▁▁ |
| RTIMOR | 0 | 1 | 16.98 | 10.32 | 0.00 | 8.20 | 15.95 | 23.90 | 64.60 | ▇▇▅▁▁ |
| TUBINC | 0 | 1 | 102.98 | 133.53 | 0.00 | 12.00 | 46.00 | 140.00 | 654.00 | ▇▂▁▁▁ |
| DPTIMM | 0 | 1 | 87.90 | 12.38 | 35.00 | 85.69 | 92.00 | 97.00 | 99.00 | ▁▁▁▃▇ |
| HEPIMM | 0 | 1 | 86.65 | 12.69 | 35.00 | 81.31 | 91.00 | 96.00 | 99.00 | ▁▁▁▃▇ |
| MEAIMM | 0 | 1 | 87.22 | 13.14 | 37.00 | 84.85 | 92.00 | 96.00 | 99.00 | ▁▁▁▃▇ |
| HOSBED | 0 | 1 | 2.99 | 2.35 | 0.20 | 1.30 | 2.57 | 3.75 | 13.71 | ▇▅▂▁▁ |
| SANSER | 0 | 1 | 77.61 | 27.61 | 8.63 | 63.90 | 91.24 | 98.65 | 100.00 | ▁▁▁▂▇ |
| TUBTRT | 0 | 1 | 77.66 | 16.93 | 0.00 | 73.00 | 82.00 | 88.00 | 100.00 | ▁▁▁▅▇ |
| URBPOP | 0 | 1 | 59.21 | 23.24 | 13.25 | 41.61 | 58.90 | 78.10 | 100.00 | ▅▆▇▇▆ |
| RURPOP | 0 | 1 | 40.79 | 23.24 | 0.00 | 21.90 | 41.10 | 58.39 | 86.75 | ▆▇▇▆▅ |
| NCOMOR | 0 | 1 | 19.99 | 8.40 | 4.40 | 13.60 | 19.85 | 24.02 | 58.40 | ▅▇▂▁▁ |
| SUIRAT | 0 | 1 | 9.34 | 9.01 | 0.00 | 3.30 | 6.95 | 11.22 | 63.00 | ▇▂▁▁▁ |
##################################
# Outlier Treatment
##################################
##################################
# Listing all Predictors
##################################
DPA.Predictors <- DPA[,!names(DPA) %in% c("COUNTRY","YEAR","LIFEXP")]
##################################
# Listing all numeric predictors
##################################
DPA.Predictors.Numeric <- DPA.Predictors[,sapply(DPA.Predictors, is.numeric)]
##################################
# Identifying outliers for the numeric predictors
##################################
OutlierCountList <- c()
for (i in 1:ncol(DPA.Predictors.Numeric)) {
Outliers <- boxplot.stats(DPA.Predictors.Numeric[,i])$out
OutlierCount <- length(Outliers)
OutlierCountList <- append(OutlierCountList,OutlierCount)
OutlierIndices <- which(DPA.Predictors.Numeric[,i] %in% c(Outliers))
print(
ggplot(DPA.Predictors.Numeric, aes(x=DPA.Predictors.Numeric[,i])) +
geom_boxplot() +
theme_bw() +
theme(axis.text.y=element_blank(),
axis.ticks.y=element_blank()) +
xlab(names(DPA.Predictors.Numeric)[i]) +
labs(title=names(DPA.Predictors.Numeric)[i],
subtitle=paste0(OutlierCount, " Outlier(s) Detected")))
}
##################################
# Formulating the histogram
# for the numeric predictors
##################################
for (i in 1:ncol(DPA.Predictors.Numeric)) {
Median <- format(round(median(DPA.Predictors.Numeric[,i],na.rm = TRUE),2), nsmall=2)
Mean <- format(round(mean(DPA.Predictors.Numeric[,i],na.rm = TRUE),2), nsmall=2)
Skewness <- format(round(skewness(DPA.Predictors.Numeric[,i],na.rm = TRUE),2), nsmall=2)
print(
ggplot(DPA.Predictors.Numeric, aes(x=DPA.Predictors.Numeric[,i])) +
geom_histogram(binwidth=1,color="black", fill="white") +
geom_vline(aes(xintercept=mean(DPA.Predictors.Numeric[,i])),
color="blue", size=1) +
geom_vline(aes(xintercept=median(DPA.Predictors.Numeric[,i])),
color="red", size=1) +
theme_bw() +
ylab("Count") +
xlab(names(DPA.Predictors.Numeric)[i]) +
labs(title=names(DPA.Predictors.Numeric)[i],
subtitle=paste0("Median = ", Median,
", Mean = ", Mean,
", Skewness = ", Skewness)))
}
##################################
# Investigating distributional anomalies
# observed for several predictors
##################################
(INFMOR_Unique <- DPA %>%
group_by(INFMOR) %>%
summarize(Distinct_INFMOR = n_distinct(COUNTRY)) %>%
arrange(desc(Distinct_INFMOR)) %>%
slice(1:5))## # A tibble: 5 x 2
## INFMOR Distinct_INFMOR
## <dbl> <int>
## 1 30.2 14
## 2 2.1 7
## 3 6.4 6
## 4 1.7 4
## 5 2.5 4(INFMOR_Unique_Country <- DPA[round(DPA$INFMOR,digits=1)==30.2,c("COUNTRY")])## [1] "Aruba" "Bermuda"
## [3] "Channel Islands" "Faroe Islands"
## [5] "French Polynesia" "Guam"
## [7] "Hong Kong SAR, China" "Kosovo"
## [9] "Liechtenstein" "Macao SAR, China"
## [11] "New Caledonia" "Puerto Rico"
## [13] "St. Martin (French part)" "Virgin Islands (U.S.)"
## [15] "Aruba" "Bermuda"
## [17] "Channel Islands" "Faroe Islands"
## [19] "French Polynesia" "Guam"
## [21] "Hong Kong SAR, China" "Kosovo"
## [23] "Liechtenstein" "Macao SAR, China"
## [25] "New Caledonia" "Puerto Rico"
## [27] "St. Martin (French part)" "Virgin Islands (U.S.)"DPA %>%
group_by(CLTECH) %>%
summarize(Distinct_CLTECH = n_distinct(COUNTRY)) %>%
arrange(desc(Distinct_CLTECH)) %>%
slice(1:5)## # A tibble: 5 x 2
## CLTECH Distinct_CLTECH
## <dbl> <int>
## 1 100 55
## 2 60.6 17
## 3 9.30 3
## 4 99.9 3
## 5 0.2 2(CLTECH_Unique_Country <- DPA[round(DPA$CLTECH,digits=1)==60.6,c("COUNTRY")])## [1] "Aruba" "Bermuda"
## [3] "Channel Islands" "Faroe Islands"
## [5] "French Polynesia" "Guam"
## [7] "Hong Kong SAR, China" "Kosovo"
## [9] "Lebanon" "Libya"
## [11] "Liechtenstein" "Macao SAR, China"
## [13] "New Caledonia" "Puerto Rico"
## [15] "St. Martin (French part)" "Virgin Islands (U.S.)"
## [17] "West Bank and Gaza" "Aruba"
## [19] "Bermuda" "Channel Islands"
## [21] "Faroe Islands" "French Polynesia"
## [23] "Guam" "Hong Kong SAR, China"
## [25] "Kosovo" "Lebanon"
## [27] "Libya" "Liechtenstein"
## [29] "Macao SAR, China" "New Caledonia"
## [31] "Puerto Rico" "St. Martin (French part)"
## [33] "Virgin Islands (U.S.)" "West Bank and Gaza"DPA %>%
group_by(RTIMOR) %>%
summarize(Distinct_RTIMOR = n_distinct(COUNTRY)) %>%
arrange(desc(Distinct_RTIMOR)) %>%
slice(1:5)## # A tibble: 5 x 2
## RTIMOR Distinct_RTIMOR
## <dbl> <int>
## 1 18.2 14
## 2 3.9 3
## 3 5.1 3
## 4 5.3 3
## 5 12.7 3(RTIMOR_Unique_Country <- DPA[round(DPA$RTIMOR,digits=1)==18.2,c("COUNTRY")])## [1] "Aruba" "Bermuda"
## [3] "Channel Islands" "Faroe Islands"
## [5] "French Polynesia" "Guam"
## [7] "Hong Kong SAR, China" "Kosovo"
## [9] "Liechtenstein" "Macao SAR, China"
## [11] "New Caledonia" "Puerto Rico"
## [13] "St. Martin (French part)" "Virgin Islands (U.S.)"
## [15] "Aruba" "Bermuda"
## [17] "Channel Islands" "Faroe Islands"
## [19] "French Polynesia" "Guam"
## [21] "Hong Kong SAR, China" "Kosovo"
## [23] "Liechtenstein" "Macao SAR, China"
## [25] "New Caledonia" "Puerto Rico"
## [27] "St. Martin (French part)" "Virgin Islands (U.S.)"DPA %>%
group_by(DPTIMM) %>%
summarize(Distinct_DPTIMM = n_distinct(COUNTRY)) %>%
arrange(desc(Distinct_DPTIMM)) %>%
slice(1:5)## # A tibble: 5 x 2
## DPTIMM Distinct_DPTIMM
## <dbl> <int>
## 1 99 22
## 2 85.7 15
## 3 97 14
## 4 98 14
## 5 95 13(DPTIMM_Unique_Country <- DPA[round(DPA$DPTIMM,digits=1)==85.7,c("COUNTRY")])## [1] "Aruba" "Bermuda"
## [3] "Channel Islands" "Faroe Islands"
## [5] "French Polynesia" "Guam"
## [7] "Hong Kong SAR, China" "Kosovo"
## [9] "Liechtenstein" "Macao SAR, China"
## [11] "New Caledonia" "Puerto Rico"
## [13] "St. Martin (French part)" "Virgin Islands (U.S.)"
## [15] "West Bank and Gaza" "Aruba"
## [17] "Bermuda" "Channel Islands"
## [19] "Faroe Islands" "French Polynesia"
## [21] "Guam" "Hong Kong SAR, China"
## [23] "Kosovo" "Liechtenstein"
## [25] "Macao SAR, China" "New Caledonia"
## [27] "Puerto Rico" "St. Martin (French part)"
## [29] "Virgin Islands (U.S.)" "West Bank and Gaza"DPA %>%
group_by(HEPIMM) %>%
summarize(Distinct_HEPIMM = n_distinct(COUNTRY)) %>%
arrange(desc(Distinct_HEPIMM)) %>%
slice(1:5)## # A tibble: 5 x 2
## HEPIMM Distinct_HEPIMM
## <dbl> <int>
## 1 81.3 20
## 2 99 19
## 3 97 17
## 4 98 11
## 5 92 10(HEPIMM_Unique_Country <- DPA[round(DPA$HEPIMM,digits=1)==81.3,c("COUNTRY")])## [1] "Aruba" "Bermuda"
## [3] "Channel Islands" "Denmark"
## [5] "Faroe Islands" "Finland"
## [7] "French Polynesia" "Guam"
## [9] "Hong Kong SAR, China" "Hungary"
## [11] "Iceland" "Kosovo"
## [13] "Liechtenstein" "Macao SAR, China"
## [15] "New Caledonia" "Puerto Rico"
## [17] "Slovenia" "St. Martin (French part)"
## [19] "Virgin Islands (U.S.)" "West Bank and Gaza"
## [21] "Aruba" "Bermuda"
## [23] "Channel Islands" "Denmark"
## [25] "Faroe Islands" "Finland"
## [27] "French Polynesia" "Guam"
## [29] "Hong Kong SAR, China" "Hungary"
## [31] "Iceland" "Kosovo"
## [33] "Liechtenstein" "Macao SAR, China"
## [35] "New Caledonia" "Puerto Rico"
## [37] "Slovenia" "St. Martin (French part)"
## [39] "Virgin Islands (U.S.)" "West Bank and Gaza"DPA %>%
group_by(MEAIMM) %>%
summarize(Distinct_MEAIMM = n_distinct(COUNTRY)) %>%
arrange(desc(Distinct_MEAIMM)) %>%
slice(1:5)## # A tibble: 5 x 2
## MEAIMM Distinct_MEAIMM
## <dbl> <int>
## 1 99 24
## 2 84.9 15
## 3 95 14
## 4 96 14
## 5 98 13(MEAIMM_Unique_Country <- DPA[round(DPA$MEAIMM,digits=1)==84.9,c("COUNTRY")])## [1] "Aruba" "Bermuda"
## [3] "Channel Islands" "Faroe Islands"
## [5] "French Polynesia" "Guam"
## [7] "Hong Kong SAR, China" "Kosovo"
## [9] "Liechtenstein" "Macao SAR, China"
## [11] "New Caledonia" "Puerto Rico"
## [13] "St. Martin (French part)" "Virgin Islands (U.S.)"
## [15] "West Bank and Gaza" "Aruba"
## [17] "Bermuda" "Channel Islands"
## [19] "Faroe Islands" "French Polynesia"
## [21] "Guam" "Hong Kong SAR, China"
## [23] "Kosovo" "Liechtenstein"
## [25] "Macao SAR, China" "New Caledonia"
## [27] "Puerto Rico" "St. Martin (French part)"
## [29] "Virgin Islands (U.S.)" "West Bank and Gaza"DPA %>%
group_by(HOSBED) %>%
summarize(Distinct_HOSBED = n_distinct(COUNTRY)) %>%
arrange(desc(Distinct_HOSBED)) %>%
slice(1:5)## # A tibble: 5 x 2
## HOSBED Distinct_HOSBED
## <dbl> <int>
## 1 2.99 17
## 2 0.4 4
## 3 0.8 2
## 4 0.85 2
## 5 0.9 2(HOSBED_Unique_Country <- DPA[round(DPA$HOSBED,digits=1)==3.0,c("COUNTRY")])## [1] "Aruba" "Bermuda"
## [3] "Channel Islands" "Faroe Islands"
## [5] "French Polynesia" "Guam"
## [7] "Hong Kong SAR, China" "Kosovo"
## [9] "Liechtenstein" "Macao SAR, China"
## [11] "Namibia" "New Caledonia"
## [13] "Papua New Guinea" "Puerto Rico"
## [15] "South Sudan" "St. Martin (French part)"
## [17] "Virgin Islands (U.S.)" "West Bank and Gaza"
## [19] "Aruba" "Bermuda"
## [21] "Channel Islands" "Faroe Islands"
## [23] "French Polynesia" "Guam"
## [25] "Hong Kong SAR, China" "Kosovo"
## [27] "Liechtenstein" "Macao SAR, China"
## [29] "Namibia" "New Caledonia"
## [31] "Papua New Guinea" "Puerto Rico"
## [33] "South Sudan" "St. Martin (French part)"
## [35] "Virgin Islands (U.S.)" "West Bank and Gaza"DPA %>%
group_by(NCOMOR) %>%
summarize(Distinct_NCOMOR = n_distinct(COUNTRY)) %>%
arrange(desc(Distinct_NCOMOR)) %>%
slice(1:5)## # A tibble: 5 x 2
## NCOMOR Distinct_NCOMOR
## <dbl> <int>
## 1 22.1 15
## 2 6.8 5
## 3 13.6 5
## 4 15.2 5
## 5 17.5 5(NCOMOR_Unique_Country <- DPA[round(DPA$NCOMOR,digits=1)==22.1,c("COUNTRY")])## [1] "Aruba" "Bermuda"
## [3] "Burkina Faso" "Channel Islands"
## [5] "Faroe Islands" "French Polynesia"
## [7] "Guam" "Hong Kong SAR, China"
## [9] "Kosovo" "Liechtenstein"
## [11] "Macao SAR, China" "New Caledonia"
## [13] "Puerto Rico" "St. Martin (French part)"
## [15] "Virgin Islands (U.S.)" "West Bank and Gaza"
## [17] "Aruba" "Bermuda"
## [19] "Channel Islands" "Dominican Republic"
## [21] "Equatorial Guinea" "Estonia"
## [23] "Faroe Islands" "French Polynesia"
## [25] "Guam" "Hong Kong SAR, China"
## [27] "Kosovo" "Liechtenstein"
## [29] "Macao SAR, China" "New Caledonia"
## [31] "Puerto Rico" "Sierra Leone"
## [33] "St. Martin (French part)" "Virgin Islands (U.S.)"
## [35] "West Bank and Gaza"DPA %>%
group_by(SUIRAT) %>%
summarize(Distinct_SUIRAT = n_distinct(COUNTRY)) %>%
arrange(desc(Distinct_SUIRAT)) %>%
slice(1:5)## # A tibble: 5 x 2
## SUIRAT Distinct_SUIRAT
## <dbl> <int>
## 1 10.6 15
## 2 7.6 8
## 3 1.7 7
## 4 2 7
## 5 2.8 7(SUIRAT_Unique_Country <- DPA[round(DPA$SUIRAT,digits=1)==10.6,c("COUNTRY")])## [1] "Aruba" "Bermuda"
## [3] "Channel Islands" "Faroe Islands"
## [5] "French Polynesia" "Guam"
## [7] "Hong Kong SAR, China" "Kosovo"
## [9] "Liechtenstein" "Macao SAR, China"
## [11] "New Caledonia" "Puerto Rico"
## [13] "St. Martin (French part)" "Virgin Islands (U.S.)"
## [15] "West Bank and Gaza" "Aruba"
## [17] "Bermuda" "Channel Islands"
## [19] "Congo, Dem. Rep." "Faroe Islands"
## [21] "French Polynesia" "Guam"
## [23] "Hong Kong SAR, China" "Kosovo"
## [25] "Liechtenstein" "Macao SAR, China"
## [27] "New Caledonia" "Puerto Rico"
## [29] "St. Martin (French part)" "Virgin Islands (U.S.)"
## [31] "West Bank and Gaza"(AnomalousVariables_Unique_Country <- MEAIMM_Unique_Country)## [1] "Aruba" "Bermuda"
## [3] "Channel Islands" "Faroe Islands"
## [5] "French Polynesia" "Guam"
## [7] "Hong Kong SAR, China" "Kosovo"
## [9] "Liechtenstein" "Macao SAR, China"
## [11] "New Caledonia" "Puerto Rico"
## [13] "St. Martin (French part)" "Virgin Islands (U.S.)"
## [15] "West Bank and Gaza" "Aruba"
## [17] "Bermuda" "Channel Islands"
## [19] "Faroe Islands" "French Polynesia"
## [21] "Guam" "Hong Kong SAR, China"
## [23] "Kosovo" "Liechtenstein"
## [25] "Macao SAR, China" "New Caledonia"
## [27] "Puerto Rico" "St. Martin (French part)"
## [29] "Virgin Islands (U.S.)" "West Bank and Gaza"##################################
# Removing associated rows associated
# with anomalous variables
##################################
dim(DPA)## [1] 396 22DPA <- DPA[!(DPA$COUNTRY %in% AnomalousVariables_Unique_Country),]
dim(DPA)## [1] 366 22##################################
# Listing all Predictors
# for the updated data
##################################
DPA.Predictors <- DPA[,!names(DPA) %in% c("COUNTRY","YEAR","LIFEXP")]
##################################
# Listing all numeric predictors
# for the updated data
##################################
DPA.Predictors.Numeric <- DPA.Predictors[,sapply(DPA.Predictors, is.numeric)]1.3.3.2 Zero and Near-Zero Variance
Code Chunk | Output
##################################
# Zero and Near-Zero Variance
##################################
##################################
# Identifying columns with low variance
###################################
DPA_LowVariance <- nearZeroVar(DPA,
freqCut = 80/20,
uniqueCut = 10,
saveMetrics= TRUE)
(DPA_LowVariance[DPA_LowVariance$nzv,])## freqRatio percentUnique zeroVar nzv
## YEAR 0 0.273224 TRUE TRUEif ((nrow(DPA_LowVariance[DPA_LowVariance$nzv,]))==0){
print("No low variance predictors noted.")
} else {
print(paste0("Low variance observed for ",
(nrow(DPA_LowVariance[DPA_LowVariance$nzv,])),
" numeric variable(s) with First.Second.Mode.Ratio>4 and Unique.Count.Ratio<0.10."))
DPA_LowVarianceForRemoval <- (nrow(DPA_LowVariance[DPA_LowVariance$nzv,]))
print(paste0("Low variance can be resolved by removing ",
(nrow(DPA_LowVariance[DPA_LowVariance$nzv,])),
" numeric variable(s)."))
for (j in 1:DPA_LowVarianceForRemoval) {
DPA_LowVarianceRemovedVariable <- rownames(DPA_LowVariance[DPA_LowVariance$nzv,])[j]
print(paste0("Variable ",
j,
" for removal: ",
DPA_LowVarianceRemovedVariable))
}
DPA %>%
skim() %>%
dplyr::filter(skim_variable %in% rownames(DPA_LowVariance[DPA_LowVariance$nzv,]))
}## [1] "Low variance observed for 1 numeric variable(s) with First.Second.Mode.Ratio>4 and Unique.Count.Ratio<0.10."
## [1] "Low variance can be resolved by removing 1 numeric variable(s)."
## [1] "Variable 1 for removal: YEAR"| Name | Piped data |
| Number of rows | 366 |
| Number of columns | 22 |
| _______________________ | |
| Column type frequency: | |
| factor | 1 |
| ________________________ | |
| Group variables | None |
Variable type: factor
| skim_variable | n_missing | complete_rate | ordered | n_unique | top_counts |
|---|---|---|---|---|---|
| YEAR | 0 | 1 | FALSE | 1 | 201: 366 |
1.3.3.3 Collinearity
Code Chunk | Output
##################################
# Collinearity
##################################
##################################
# Visualizing pairwise correlation between predictors
##################################
DPA_CorrelationTest <- cor.mtest(DPA.Predictors.Numeric,
method = "pearson",
conf.level = .95)
corrplot(cor(DPA.Predictors.Numeric,
method = "pearson",
use="pairwise.complete.obs"),
method = "circle",
type = "upper",
order = "original",
tl.col = "black",
tl.cex = 0.75,
tl.srt = 90,
sig.level = 0.05,
p.mat = DPA_CorrelationTest$p,
insig = "blank")
##################################
# Identifying the highly correlated variables
##################################
DPA_Correlation <- cor(DPA.Predictors.Numeric,
method = "pearson",
use="pairwise.complete.obs")
(DPA_HighlyCorrelatedCount <- sum(abs(DPA_Correlation[upper.tri(DPA_Correlation)]) > 0.75))## [1] 8if (DPA_HighlyCorrelatedCount == 0) {
print("No highly correlated predictors noted.")
} else {
print(paste0("High correlation observed for ",
(DPA_HighlyCorrelatedCount),
" pairs of numeric variable(s) with Correlation.Coefficient>0.75."))
(DPA_HighlyCorrelatedPairs <- corr_cross(DPA.Predictors.Numeric,
max_pvalue = 0.05,
top = DPA_HighlyCorrelatedCount,
rm.na = TRUE,
grid = FALSE
))
}## [1] "High correlation observed for 8 pairs of numeric variable(s) with Correlation.Coefficient>0.75."
if (DPA_HighlyCorrelatedCount > 0) {
DPA_HighlyCorrelated <- findCorrelation(DPA_Correlation, cutoff = 0.75)
(DPA_HighlyCorrelatedForRemoval <- length(DPA_HighlyCorrelated))
print(paste0("High correlation can be resolved by removing ",
(DPA_HighlyCorrelatedForRemoval),
" numeric variable(s)."))
for (j in 1:DPA_HighlyCorrelatedForRemoval) {
DPA_HighlyCorrelatedRemovedVariable <- colnames(DPA.Predictors.Numeric)[DPA_HighlyCorrelated[j]]
print(paste0("Variable ",
j,
" for removal: ",
DPA_HighlyCorrelatedRemovedVariable))
}
}## [1] "High correlation can be resolved by removing 6 numeric variable(s)."
## [1] "Variable 1 for removal: INFMOR"
## [1] "Variable 2 for removal: CLTECH"
## [1] "Variable 3 for removal: URBPOP"
## [1] "Variable 4 for removal: DPTIMM"
## [1] "Variable 5 for removal: MEAIMM"
## [1] "Variable 6 for removal: GNI"1.3.3.4 Linear Dependencies
Code Chunk | Output
##################################
# Linear Dependencies
##################################
##################################
# Finding linear dependencies
##################################
DPA_LinearlyDependent <- findLinearCombos(DPA.Predictors.Numeric)
##################################
# Identifying the linearly dependent variables
##################################
DPA_LinearlyDependent <- findLinearCombos(DPA.Predictors.Numeric)
(DPA_LinearlyDependentCount <- length(DPA_LinearlyDependent$linearCombos))## [1] 0if (DPA_LinearlyDependentCount == 0) {
print("No linearly dependent predictors noted.")
} else {
print(paste0("Linear dependency observed for ",
(DPA_LinearlyDependentCount),
" subset(s) of numeric variable(s)."))
for (i in 1:DPA_LinearlyDependentCount) {
DPA_LinearlyDependentSubset <- colnames(DPA.Predictors.Numeric)[DPA_LinearlyDependent$linearCombos[[i]]]
print(paste0("Linear dependent variable(s) for subset ",
i,
" include: ",
DPA_LinearlyDependentSubset))
}
}## [1] "No linearly dependent predictors noted."##################################
# Identifying the linearly dependent variables for removal
##################################
if (DPA_LinearlyDependentCount > 0) {
DPA_LinearlyDependent <- findLinearCombos(DPA.Predictors.Numeric)
DPA_LinearlyDependentForRemoval <- length(DPA_LinearlyDependent$remove)
print(paste0("Linear dependency can be resolved by removing ",
(DPA_LinearlyDependentForRemoval),
" numeric variable(s)."))
for (j in 1:DPA_LinearlyDependentForRemoval) {
DPA_LinearlyDependentRemovedVariable <- colnames(DPA.Predictors.Numeric)[DPA_LinearlyDependent$remove[j]]
print(paste0("Variable ",
j,
" for removal: ",
DPA_LinearlyDependentRemovedVariable))
}
}1.3.3.5 Shape Transformation
Code Chunk | Output
##################################
# Shape Transformation
##################################
##################################
# Applying a Box-Cox transformation
##################################
DPA_BoxCox <- preProcess(DPA.Predictors.Numeric, method = c("BoxCox"))
DPA_BoxCoxTransformed <- predict(DPA_BoxCox, DPA.Predictors.Numeric)
for (i in 1:ncol(DPA_BoxCoxTransformed)) {
Median <- format(round(median(DPA_BoxCoxTransformed[,i],na.rm = TRUE),2), nsmall=2)
Mean <- format(round(mean(DPA_BoxCoxTransformed[,i],na.rm = TRUE),2), nsmall=2)
Skewness <- format(round(skewness(DPA_BoxCoxTransformed[,i],na.rm = TRUE),2), nsmall=2)
print(
ggplot(DPA_BoxCoxTransformed, aes(x=DPA_BoxCoxTransformed[,i])) +
geom_histogram(binwidth=1,color="black", fill="white") +
geom_vline(aes(xintercept=mean(DPA_BoxCoxTransformed[,i])),
color="blue", size=1) +
geom_vline(aes(xintercept=median(DPA_BoxCoxTransformed[,i])),
color="red", size=1) +
theme_bw() +
ylab("Count") +
xlab(names(DPA_BoxCoxTransformed)[i]) +
labs(title=names(DPA_BoxCoxTransformed)[i],
subtitle=paste0("Median = ", Median,
", Mean = ", Mean,
", Skewness = ", Skewness)))
}
DPA_BoxCoxTransformed <- cbind(DPA_BoxCoxTransformed,DPA[,c("COUNTRY",
"YEAR",
"GENDER",
"LIFEXP")])1.3.3.6 Pre-Processed Dataset
Code Chunk | Output
##################################
# Creating the pre-modelling
# train set
##################################
PMA <- DPA_BoxCoxTransformed[,!names(DPA_BoxCoxTransformed) %in% c("YEAR",
"GNI",
"DPTIMM",
"MEAIMM",
"RURPOP",
"SANSER",
"RTIMOR")]
##################################
# Gathering descriptive statistics
##################################
(PMA_Skimmed <- skim(PMA))| Name | PMA |
| Number of rows | 366 |
| Number of columns | 15 |
| _______________________ | |
| Column type frequency: | |
| character | 1 |
| factor | 1 |
| numeric | 13 |
| ________________________ | |
| Group variables | None |
Variable type: character
| skim_variable | n_missing | complete_rate | min | max | empty | n_unique | whitespace |
|---|---|---|---|---|---|---|---|
| COUNTRY | 0 | 1 | 4 | 30 | 0 | 183 | 0 |
Variable type: factor
| skim_variable | n_missing | complete_rate | ordered | n_unique | top_counts |
|---|---|---|---|---|---|
| GENDER | 0 | 1 | FALSE | 2 | Mal: 183, Fem: 183 |
Variable type: numeric
| skim_variable | n_missing | complete_rate | mean | sd | p0 | p25 | p50 | p75 | p100 | hist |
|---|---|---|---|---|---|---|---|---|---|---|
| UNEMPR | 0 | 1 | 2.12 | 1.21 | -2.05 | 1.42 | 2.00 | 2.90 | 5.26 | ▁▂▇▅▂ |
| INFMOR | 0 | 1 | 2.55 | 1.06 | 0.34 | 1.70 | 2.62 | 3.48 | 4.49 | ▅▆▇▇▆ |
| GDP | 0 | 1 | 3.89 | 2.26 | -1.67 | 2.55 | 3.80 | 5.55 | 10.05 | ▂▇▇▆▁ |
| CLTECH | 0 | 1 | 66.26 | 37.75 | 0.00 | 30.47 | 84.60 | 100.00 | 100.00 | ▃▁▁▂▇ |
| PERCAP | 0 | 1 | 1.77 | 1.41 | -1.48 | 0.65 | 1.80 | 2.85 | 4.73 | ▂▆▇▆▃ |
| TUBINC | 0 | 1 | 105.76 | 137.53 | 0.00 | 12.00 | 46.00 | 149.00 | 654.00 | ▇▂▁▁▁ |
| HEPIMM | 0 | 1 | 3877.73 | 1003.98 | 612.00 | 3339.73 | 4231.50 | 4704.00 | 4900.00 | ▁▁▁▃▇ |
| HOSBED | 0 | 1 | 0.77 | 0.85 | -1.61 | 0.15 | 0.84 | 1.40 | 2.62 | ▁▅▇▇▃ |
| TUBTRT | 0 | 1 | 77.90 | 17.27 | 0.00 | 73.25 | 83.00 | 88.00 | 100.00 | ▁▁▁▅▇ |
| URBPOP | 0 | 1 | 58.40 | 22.69 | 13.25 | 40.37 | 58.52 | 77.78 | 100.00 | ▅▆▇▇▅ |
| NCOMOR | 0 | 1 | 4.67 | 1.08 | 1.87 | 3.93 | 4.70 | 5.38 | 7.96 | ▂▅▇▃▁ |
| SUIRAT | 0 | 1 | 9.23 | 9.37 | 0.00 | 3.20 | 6.30 | 11.80 | 63.00 | ▇▂▁▁▁ |
| LIFEXP | 0 | 1 | 72.54 | 7.77 | 51.20 | 66.99 | 73.69 | 78.58 | 87.45 | ▁▃▆▇▅ |
1.3.4 Data Exploration
Code Chunk | Output
##################################
# Loading dataset
##################################
PME <- PMA
PME.Numeric <- PME[,sapply(PME, is.numeric), drop = FALSE]
##################################
# Listing all Predictors
##################################
PME.Predictors <- PME[,!names(PME) %in% c("COUNTRY","LIFEXP")]
##################################
# Listing all numeric Predictors
##################################
PME.Predictors.Numeric <- PME.Predictors[,sapply(PME.Predictors, is.numeric), drop = FALSE]
ncol(PME.Predictors.Numeric)## [1] 12##################################
# Listing all numeric Predictors
##################################
PME.Predictors.Factor <- PME.Predictors[,sapply(PME.Predictors, is.factor), drop = FALSE]
ncol(PME.Predictors.Factor)## [1] 1##################################
# Formulating the scatter plot
##################################
featurePlot(x = PME.Predictors.Numeric,
y = PME$LIFEXP,
plot = "scatter",
type = c("p", "smooth"),
span = .5,
layout = c(4, 3))
##################################
# Formulating the box plot
##################################
featurePlot(x = PME.Numeric,
y = PME$GENDER,
plot = "box",
scales = list(x = list(relation="free", rot = 90),
y = list(relation="free")),
adjust = 1.5,
layout = c(4, 4))
1.3.5 Feature Selection
1.3.5.1 Locally Weighted Scatterplot Smoothing Pseudo-R-Squared (LOWESSPR)
Code Chunk | Output
##################################
# Evaluating model-independent
# feature importance metrics
##################################
##################################
# Obtaining the LOWESSPR pseudo-R-Squared
##################################
FE_LOWESSPR <- filterVarImp(x = PME.Numeric[,!names(PME.Numeric) %in% c("LIFEXP")],
y = PME$LIFEXP,
nonpara = TRUE)
##################################
# Formulating the summary table
##################################
FE_LOWESSPR_Summary <- FE_LOWESSPR
FE_LOWESSPR_Summary$Predictor <- rownames(FE_LOWESSPR)
names(FE_LOWESSPR_Summary)[1] <- "LOWESSPR"
FE_LOWESSPR_Summary$Metric <- rep("LOWESSPR",nrow(FE_LOWESSPR))
FE_LOWESSPR_Summary## LOWESSPR Predictor Metric
## UNEMPR 0.03820513 UNEMPR LOWESSPR
## INFMOR 0.82577754 INFMOR LOWESSPR
## GDP 0.25709988 GDP LOWESSPR
## CLTECH 0.58356246 CLTECH LOWESSPR
## PERCAP 0.62203760 PERCAP LOWESSPR
## TUBINC 0.51694851 TUBINC LOWESSPR
## HEPIMM 0.18580579 HEPIMM LOWESSPR
## HOSBED 0.34778666 HOSBED LOWESSPR
## TUBTRT 0.10269120 TUBTRT LOWESSPR
## URBPOP 0.35270735 URBPOP LOWESSPR
## NCOMOR 0.59824235 NCOMOR LOWESSPR
## SUIRAT 0.06391291 SUIRAT LOWESSPR##################################
# Exploring predictor performance
# using LOWESS
##################################
dotplot(Predictor ~ LOWESSPR | Metric,
FE_LOWESSPR_Summary,
origin = 0,
type = c("p", "h"),
pch = 16,
cex = 2,
alpha = 0.45,
prepanel = function(x, y) {
list(ylim = levels(reorder(y, x)))
},
panel = function(x, y, ...) {
panel.dotplot(x, reorder(y, x), ...)
})
1.3.5.2 Pearson’s Correlation Coefficient (PCC)
Code Chunk | Output
##################################
# Obtaining the Pearson correlation coefficient
##################################
(FE_PCC <- abs(cor(PME.Numeric, method="pearson")[-13,13]))## UNEMPR INFMOR GDP CLTECH PERCAP TUBINC HEPIMM HOSBED
## 0.0173876 0.8799932 0.4607543 0.7531760 0.7852114 0.5904533 0.4310520 0.5632423
## TUBTRT URBPOP NCOMOR SUIRAT
## 0.3204547 0.5751200 0.7343442 0.1214945##################################
# Formulating the summary table
##################################
FE_PCC_Summary <- data.frame(Predictor = names(PME.Numeric)[1:(ncol(PME.Numeric)-1)],
PCC = FE_PCC,
Metric = rep("PCC", length(FE_PCC)))
FE_PCC_Summary## Predictor PCC Metric
## UNEMPR UNEMPR 0.0173876 PCC
## INFMOR INFMOR 0.8799932 PCC
## GDP GDP 0.4607543 PCC
## CLTECH CLTECH 0.7531760 PCC
## PERCAP PERCAP 0.7852114 PCC
## TUBINC TUBINC 0.5904533 PCC
## HEPIMM HEPIMM 0.4310520 PCC
## HOSBED HOSBED 0.5632423 PCC
## TUBTRT TUBTRT 0.3204547 PCC
## URBPOP URBPOP 0.5751200 PCC
## NCOMOR NCOMOR 0.7343442 PCC
## SUIRAT SUIRAT 0.1214945 PCC##################################
# Exploring predictor performance
# using PCC
##################################
dotplot(Predictor ~ PCC | Metric,
FE_PCC_Summary,
origin = 0,
type = c("p", "h"),
pch = 16,
cex = 2,
alpha = 0.45,
prepanel = function(x, y) {
list(ylim = levels(reorder(y, x)))
},
panel = function(x, y, ...) {
panel.dotplot(x, reorder(y, x), ...)
})
1.3.5.3 Spearman’s Rank Correlation Coefficient (SRCC)
Code Chunk | Output
##################################
# Obtaining the Spearman's rank correlation coefficient
##################################
(FE_SRCC <- abs(cor(PME.Numeric, method="spearman")[-13,13]))## UNEMPR INFMOR GDP CLTECH PERCAP TUBINC
## 0.007171824 0.891693321 0.502535269 0.784386965 0.798496072 0.716895672
## HEPIMM HOSBED TUBTRT URBPOP NCOMOR SUIRAT
## 0.378587047 0.555525640 0.335530704 0.602733552 0.791705740 0.116688271##################################
# Formulating the summary table
##################################
FE_SRCC_Summary <- data.frame(Predictor = names(PME.Numeric)[1:(ncol(PME.Numeric)-1)],
SRCC = FE_SRCC,
Metric = rep("SRCC", length(FE_SRCC)))
FE_SRCC_Summary## Predictor SRCC Metric
## UNEMPR UNEMPR 0.007171824 SRCC
## INFMOR INFMOR 0.891693321 SRCC
## GDP GDP 0.502535269 SRCC
## CLTECH CLTECH 0.784386965 SRCC
## PERCAP PERCAP 0.798496072 SRCC
## TUBINC TUBINC 0.716895672 SRCC
## HEPIMM HEPIMM 0.378587047 SRCC
## HOSBED HOSBED 0.555525640 SRCC
## TUBTRT TUBTRT 0.335530704 SRCC
## URBPOP URBPOP 0.602733552 SRCC
## NCOMOR NCOMOR 0.791705740 SRCC
## SUIRAT SUIRAT 0.116688271 SRCC##################################
# Exploring predictor performance
# using SRCC
##################################
dotplot(Predictor ~ SRCC | Metric,
FE_SRCC_Summary,
origin = 0,
type = c("p", "h"),
pch = 16,
cex = 2,
alpha = 0.45,
prepanel = function(x, y) {
list(ylim = levels(reorder(y, x)))
},
panel = function(x, y, ...) {
panel.dotplot(x, reorder(y, x), ...)
})
1.3.5.4 Maximal Information Coefficient (MIC)
Code Chunk | Output
##################################
# Obtaining the maximal information coefficient
##################################
FE_MIC <- mine(x = PME.Numeric[,!names(PME.Numeric) %in% c("LIFEXP")],
y = PME$LIFEXP)$MIC
##################################
# Formulating the summary table
##################################
FE_MIC_Summary <- data.frame(Predictor = names(PME.Numeric)[1:(ncol(PME.Numeric)-1)],
MIC = FE_MIC[,1],
Metric = rep("MIC", length(FE_MIC)))
FE_MIC_Summary## Predictor MIC Metric
## 1 UNEMPR 0.1884410 MIC
## 2 INFMOR 0.6990431 MIC
## 3 GDP 0.3199394 MIC
## 4 CLTECH 0.5129197 MIC
## 5 PERCAP 0.5532144 MIC
## 6 TUBINC 0.5141625 MIC
## 7 HEPIMM 0.2656190 MIC
## 8 HOSBED 0.3706681 MIC
## 9 TUBTRT 0.2392547 MIC
## 10 URBPOP 0.4107627 MIC
## 11 NCOMOR 0.6458233 MIC
## 12 SUIRAT 0.2314843 MIC##################################
# Exploring predictor performance
# using MIC
##################################
dotplot(Predictor ~ MIC | Metric,
FE_MIC_Summary,
origin = 0,
type = c("p", "h"),
pch = 16,
cex = 2,
alpha = 0.45,
prepanel = function(x, y) {
list(ylim = levels(reorder(y, x)))
},
panel = function(x, y, ...) {
panel.dotplot(x, reorder(y, x), ...)
})
1.3.5.5 Relief Values (RV)
Code Chunk | Output
##################################
# Obtaining the relief values
##################################
FE_RV <- attrEval(LIFEXP ~ .,
data = PME.Numeric,
estimator = "RReliefFequalK")
##################################
# Formulating the summary table
##################################
FE_RV_Summary <- data.frame(Predictor = names(FE_RV),
RV = FE_RV,
Metric = rep("RV", length(FE_RV)))
FE_RV_Summary## Predictor RV Metric
## UNEMPR UNEMPR 0.002897323 RV
## INFMOR INFMOR 0.092217298 RV
## GDP GDP -0.116882500 RV
## CLTECH CLTECH 0.036861936 RV
## PERCAP PERCAP -0.037386041 RV
## TUBINC TUBINC 0.066678316 RV
## HEPIMM HEPIMM -0.031239659 RV
## HOSBED HOSBED -0.052866119 RV
## TUBTRT TUBTRT -0.149965561 RV
## URBPOP URBPOP -0.105354115 RV
## NCOMOR NCOMOR 0.279415774 RV
## SUIRAT SUIRAT 0.110919970 RV##################################
# Exploring predictor performance
##################################
dotplot(Predictor ~ RV | Metric,
FE_RV_Summary,
origin = 0,
type = c("p", "h"),
pch = 16,
cex = 2,
alpha = 0.45,
prepanel = function(x, y) {
list(ylim = levels(reorder(y, x)))
},
panel = function(x, y, ...) {
panel.dotplot(x, reorder(y, x), ...)
})
1.3.6 Model Development and Performance Estimation
1.3.6.1 Stochastic Gradient Boosting (GBM)
Code Chunk | Output
##################################
# Preparing the dataset for
# model development and test
##################################
set.seed(12345678)
trainIndex <- createDataPartition(PME$LIFEXP,
p = 0.8,
list = FALSE,
times = 1)
##################################
# Formulating the model development data
##################################
MD <- PME[ trainIndex,]
##################################
# Formulating the model test data
##################################
MT <- PME[-trainIndex,]
##################################
# Preparing the dataset for
# model development
##################################
MD <- MD[,c("GENDER","INFMOR","PERCAP","CLTECH","NCOMOR","LIFEXP")]
MD.Model.Predictors <- MD[,c("GENDER","INFMOR","PERCAP","CLTECH","NCOMOR")]
##################################
# Preparing the dataset for
# model test
##################################
MT <- MT[,c("GENDER","INFMOR","PERCAP","CLTECH","NCOMOR","LIFEXP")]
MT.Model.Predictors <- MT[,c("GENDER","INFMOR","PERCAP","CLTECH","NCOMOR")]
##################################
# Creating consistent fold assignments
# for the 10-Fold Cross Validation process
##################################
set.seed(12345678)
KFold_Indices <- createFolds(MD$LIFEXP,
k = 10,
returnTrain=TRUE)
KFold_Control <- trainControl(method="cv",
index=KFold_Indices)
##################################
# Defining the model hyperparameter values
# for the GBM model
##################################
GBM_Grid = expand.grid(n.trees = c(100, 200, 300),
interaction.depth = c(1, 3, 5),
shrinkage = c(0.10,0.05,0.01),
n.minobsinnode = c(15,10,5))
##################################
# Running the GBM model
# by setting the caret method to 'gbm'
##################################
set.seed(12345678)
GBM_Tune <- train(x = MD.Model.Predictors,
y = MD$LIFEXP,
method = "gbm",
tuneGrid = GBM_Grid,
trControl = KFold_Control)## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 62.1227 nan 0.0100 0.7693
## 2 61.3337 nan 0.0100 0.6983
## 3 60.6061 nan 0.0100 0.6633
## 4 59.7836 nan 0.0100 0.7341
## 5 59.0098 nan 0.0100 0.7139
## 6 58.2788 nan 0.0100 0.6988
## 7 57.5535 nan 0.0100 0.7128
## 8 56.9286 nan 0.0100 0.7258
## 9 56.1944 nan 0.0100 0.6810
## 10 55.5511 nan 0.0100 0.7079
## 20 49.5540 nan 0.0100 0.5830
## 40 39.9875 nan 0.0100 0.3742
## 60 32.7024 nan 0.0100 0.2837
## 80 27.1565 nan 0.0100 0.2605
## 100 22.8699 nan 0.0100 0.1831
## 120 19.6043 nan 0.0100 0.1340
## 140 16.9933 nan 0.0100 0.1047
## 160 14.8505 nan 0.0100 0.0993
## 180 13.2168 nan 0.0100 0.0699
## 200 11.8566 nan 0.0100 0.0406
## 220 10.7300 nan 0.0100 0.0462
## 240 9.7666 nan 0.0100 0.0397
## 260 8.9376 nan 0.0100 0.0338
## 280 8.2565 nan 0.0100 0.0243
## 300 7.6740 nan 0.0100 0.0186
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 62.0988 nan 0.0100 0.7940
## 2 61.3156 nan 0.0100 0.7742
## 3 60.5943 nan 0.0100 0.7824
## 4 59.7818 nan 0.0100 0.7272
## 5 59.0743 nan 0.0100 0.7264
## 6 58.3737 nan 0.0100 0.6166
## 7 57.6752 nan 0.0100 0.6979
## 8 56.9330 nan 0.0100 0.7227
## 9 56.2539 nan 0.0100 0.7127
## 10 55.5820 nan 0.0100 0.6156
## 20 49.4017 nan 0.0100 0.5509
## 40 39.8044 nan 0.0100 0.3929
## 60 32.6954 nan 0.0100 0.2998
## 80 27.0214 nan 0.0100 0.2255
## 100 22.8076 nan 0.0100 0.1919
## 120 19.5829 nan 0.0100 0.1334
## 140 16.8504 nan 0.0100 0.0961
## 160 14.7563 nan 0.0100 0.0761
## 180 13.0689 nan 0.0100 0.0810
## 200 11.6840 nan 0.0100 0.0492
## 220 10.5358 nan 0.0100 0.0446
## 240 9.5983 nan 0.0100 0.0374
## 260 8.8306 nan 0.0100 0.0197
## 280 8.1503 nan 0.0100 0.0261
## 300 7.6018 nan 0.0100 0.0186
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 62.1245 nan 0.0100 0.8097
## 2 61.2860 nan 0.0100 0.8222
## 3 60.5177 nan 0.0100 0.7625
## 4 59.8337 nan 0.0100 0.7230
## 5 59.1519 nan 0.0100 0.7023
## 6 58.3911 nan 0.0100 0.6609
## 7 57.7117 nan 0.0100 0.7349
## 8 56.9769 nan 0.0100 0.7364
## 9 56.3176 nan 0.0100 0.6742
## 10 55.5860 nan 0.0100 0.6828
## 20 49.3356 nan 0.0100 0.5617
## 40 39.8248 nan 0.0100 0.4355
## 60 32.5515 nan 0.0100 0.3474
## 80 27.0736 nan 0.0100 0.2165
## 100 22.8876 nan 0.0100 0.1807
## 120 19.5812 nan 0.0100 0.1292
## 140 17.0003 nan 0.0100 0.0993
## 160 14.9433 nan 0.0100 0.0780
## 180 13.2105 nan 0.0100 0.0633
## 200 11.7907 nan 0.0100 0.0527
## 220 10.6580 nan 0.0100 0.0298
## 240 9.7092 nan 0.0100 0.0303
## 260 8.9433 nan 0.0100 0.0293
## 280 8.2702 nan 0.0100 0.0224
## 300 7.6993 nan 0.0100 0.0110
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 61.8484 nan 0.0100 1.0058
## 2 60.8409 nan 0.0100 0.9750
## 3 59.8266 nan 0.0100 1.0107
## 4 58.8552 nan 0.0100 0.9944
## 5 57.8969 nan 0.0100 0.8557
## 6 56.9014 nan 0.0100 1.0323
## 7 56.0024 nan 0.0100 0.8546
## 8 55.1274 nan 0.0100 0.9597
## 9 54.1694 nan 0.0100 0.9515
## 10 53.3205 nan 0.0100 0.8718
## 20 45.4893 nan 0.0100 0.7193
## 40 33.3136 nan 0.0100 0.4897
## 60 25.1491 nan 0.0100 0.2779
## 80 19.1531 nan 0.0100 0.2291
## 100 15.0084 nan 0.0100 0.1369
## 120 12.0067 nan 0.0100 0.1216
## 140 9.8430 nan 0.0100 0.0754
## 160 8.2437 nan 0.0100 0.0535
## 180 7.1110 nan 0.0100 0.0512
## 200 6.2282 nan 0.0100 0.0366
## 220 5.6014 nan 0.0100 0.0207
## 240 5.1055 nan 0.0100 0.0170
## 260 4.7446 nan 0.0100 0.0102
## 280 4.4574 nan 0.0100 0.0061
## 300 4.2377 nan 0.0100 -0.0009
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 61.8964 nan 0.0100 0.9073
## 2 60.9011 nan 0.0100 0.9375
## 3 59.9218 nan 0.0100 0.8415
## 4 58.9196 nan 0.0100 1.0333
## 5 57.9778 nan 0.0100 0.9587
## 6 57.0334 nan 0.0100 0.9181
## 7 56.1133 nan 0.0100 0.9913
## 8 55.2123 nan 0.0100 0.8484
## 9 54.3694 nan 0.0100 0.7972
## 10 53.4861 nan 0.0100 0.8989
## 20 45.4135 nan 0.0100 0.7464
## 40 33.3772 nan 0.0100 0.4940
## 60 25.0348 nan 0.0100 0.3264
## 80 19.1679 nan 0.0100 0.2903
## 100 15.0754 nan 0.0100 0.2075
## 120 12.1305 nan 0.0100 0.1155
## 140 9.9898 nan 0.0100 0.0771
## 160 8.4204 nan 0.0100 0.0615
## 180 7.2428 nan 0.0100 0.0401
## 200 6.3844 nan 0.0100 0.0321
## 220 5.7542 nan 0.0100 0.0188
## 240 5.2573 nan 0.0100 0.0117
## 260 4.9011 nan 0.0100 0.0102
## 280 4.6129 nan 0.0100 0.0003
## 300 4.4132 nan 0.0100 0.0027
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 61.8232 nan 0.0100 1.0853
## 2 60.7615 nan 0.0100 1.0726
## 3 59.7809 nan 0.0100 1.0614
## 4 58.7383 nan 0.0100 0.9429
## 5 57.7943 nan 0.0100 1.0443
## 6 56.8382 nan 0.0100 0.9865
## 7 55.9356 nan 0.0100 0.9874
## 8 55.0589 nan 0.0100 0.8711
## 9 54.1475 nan 0.0100 0.8512
## 10 53.2693 nan 0.0100 0.8231
## 20 45.5233 nan 0.0100 0.7284
## 40 33.3373 nan 0.0100 0.5334
## 60 25.0338 nan 0.0100 0.3390
## 80 19.1644 nan 0.0100 0.2640
## 100 15.0638 nan 0.0100 0.1606
## 120 12.1221 nan 0.0100 0.1083
## 140 10.0718 nan 0.0100 0.0768
## 160 8.5848 nan 0.0100 0.0642
## 180 7.4393 nan 0.0100 0.0463
## 200 6.5853 nan 0.0100 0.0221
## 220 5.9490 nan 0.0100 0.0210
## 240 5.4649 nan 0.0100 0.0117
## 260 5.0887 nan 0.0100 0.0037
## 280 4.8247 nan 0.0100 -0.0003
## 300 4.6158 nan 0.0100 0.0061
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 61.8567 nan 0.0100 1.0966
## 2 60.7933 nan 0.0100 1.1470
## 3 59.7505 nan 0.0100 1.1401
## 4 58.7434 nan 0.0100 0.9251
## 5 57.7391 nan 0.0100 1.0180
## 6 56.7267 nan 0.0100 1.0002
## 7 55.7543 nan 0.0100 0.9079
## 8 54.7552 nan 0.0100 0.9434
## 9 53.8335 nan 0.0100 0.9930
## 10 52.9253 nan 0.0100 0.8661
## 20 44.5694 nan 0.0100 0.6188
## 40 32.0461 nan 0.0100 0.5462
## 60 23.4480 nan 0.0100 0.3912
## 80 17.4106 nan 0.0100 0.2258
## 100 13.2948 nan 0.0100 0.1607
## 120 10.4197 nan 0.0100 0.0962
## 140 8.3610 nan 0.0100 0.0711
## 160 6.9310 nan 0.0100 0.0478
## 180 5.9367 nan 0.0100 0.0349
## 200 5.1993 nan 0.0100 0.0156
## 220 4.6451 nan 0.0100 0.0128
## 240 4.2325 nan 0.0100 0.0075
## 260 3.9176 nan 0.0100 0.0065
## 280 3.6660 nan 0.0100 0.0060
## 300 3.4646 nan 0.0100 0.0044
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 61.8350 nan 0.0100 1.0879
## 2 60.7413 nan 0.0100 1.0083
## 3 59.6438 nan 0.0100 1.0774
## 4 58.5917 nan 0.0100 0.9423
## 5 57.5165 nan 0.0100 0.9410
## 6 56.5438 nan 0.0100 0.9834
## 7 55.5377 nan 0.0100 0.9295
## 8 54.5458 nan 0.0100 0.8482
## 9 53.5609 nan 0.0100 0.8309
## 10 52.6312 nan 0.0100 0.8911
## 20 44.3743 nan 0.0100 0.7673
## 40 31.8672 nan 0.0100 0.4832
## 60 23.2646 nan 0.0100 0.3351
## 80 17.3926 nan 0.0100 0.2176
## 100 13.2413 nan 0.0100 0.1499
## 120 10.4238 nan 0.0100 0.1254
## 140 8.4523 nan 0.0100 0.0771
## 160 7.0427 nan 0.0100 0.0488
## 180 6.0645 nan 0.0100 0.0377
## 200 5.3543 nan 0.0100 0.0211
## 220 4.8252 nan 0.0100 0.0136
## 240 4.4383 nan 0.0100 0.0061
## 260 4.1290 nan 0.0100 0.0058
## 280 3.9063 nan 0.0100 0.0012
## 300 3.7428 nan 0.0100 0.0009
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 61.7795 nan 0.0100 1.1843
## 2 60.7281 nan 0.0100 1.0092
## 3 59.6082 nan 0.0100 1.0506
## 4 58.5483 nan 0.0100 1.0928
## 5 57.5230 nan 0.0100 1.0513
## 6 56.5212 nan 0.0100 0.9589
## 7 55.5274 nan 0.0100 1.0002
## 8 54.6074 nan 0.0100 0.9283
## 9 53.6685 nan 0.0100 1.0439
## 10 52.7635 nan 0.0100 0.9825
## 20 44.5357 nan 0.0100 0.7147
## 40 32.1380 nan 0.0100 0.4751
## 60 23.5894 nan 0.0100 0.3759
## 80 17.6773 nan 0.0100 0.2295
## 100 13.6115 nan 0.0100 0.1471
## 120 10.7324 nan 0.0100 0.0983
## 140 8.7455 nan 0.0100 0.0588
## 160 7.3378 nan 0.0100 0.0603
## 180 6.3314 nan 0.0100 0.0440
## 200 5.6190 nan 0.0100 0.0307
## 220 5.1231 nan 0.0100 0.0156
## 240 4.7532 nan 0.0100 0.0019
## 260 4.4648 nan 0.0100 0.0116
## 280 4.2383 nan 0.0100 0.0045
## 300 4.0646 nan 0.0100 -0.0003
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 59.0285 nan 0.0500 3.7847
## 2 55.2280 nan 0.0500 3.4680
## 3 52.0630 nan 0.0500 3.0705
## 4 49.0940 nan 0.0500 2.7595
## 5 46.3520 nan 0.0500 2.7705
## 6 43.8739 nan 0.0500 2.3552
## 7 41.5192 nan 0.0500 2.3073
## 8 39.4099 nan 0.0500 2.0434
## 9 37.5930 nan 0.0500 1.7674
## 10 35.7126 nan 0.0500 1.8581
## 20 22.3387 nan 0.0500 0.9188
## 40 11.6088 nan 0.0500 0.3617
## 60 7.4938 nan 0.0500 0.0612
## 80 5.7633 nan 0.0500 0.0581
## 100 4.9643 nan 0.0500 0.0286
## 120 4.5650 nan 0.0500 0.0075
## 140 4.3793 nan 0.0500 -0.0163
## 160 4.2708 nan 0.0500 -0.0097
## 180 4.1721 nan 0.0500 -0.0002
## 200 4.1168 nan 0.0500 -0.0102
## 220 4.0538 nan 0.0500 -0.0184
## 240 4.0048 nan 0.0500 -0.0171
## 260 3.9464 nan 0.0500 -0.0345
## 280 3.9020 nan 0.0500 -0.0108
## 300 3.8507 nan 0.0500 -0.0057
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 59.2877 nan 0.0500 3.7427
## 2 55.8983 nan 0.0500 3.4934
## 3 52.5998 nan 0.0500 3.1757
## 4 49.7127 nan 0.0500 2.8915
## 5 46.9318 nan 0.0500 2.6198
## 6 44.5185 nan 0.0500 2.1359
## 7 41.9770 nan 0.0500 2.3058
## 8 39.8183 nan 0.0500 2.3300
## 9 37.9860 nan 0.0500 1.7638
## 10 35.9557 nan 0.0500 1.8572
## 20 22.8119 nan 0.0500 0.8712
## 40 11.5042 nan 0.0500 0.3279
## 60 7.5010 nan 0.0500 0.0908
## 80 5.8261 nan 0.0500 0.0300
## 100 5.0749 nan 0.0500 -0.0070
## 120 4.6814 nan 0.0500 -0.0055
## 140 4.5318 nan 0.0500 -0.0097
## 160 4.3929 nan 0.0500 -0.0004
## 180 4.3060 nan 0.0500 -0.0035
## 200 4.2341 nan 0.0500 -0.0337
## 220 4.1675 nan 0.0500 -0.0103
## 240 4.1152 nan 0.0500 -0.0162
## 260 4.0526 nan 0.0500 -0.0057
## 280 4.0087 nan 0.0500 -0.0063
## 300 3.9599 nan 0.0500 -0.0075
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 58.9434 nan 0.0500 4.1606
## 2 55.6102 nan 0.0500 3.6048
## 3 52.3485 nan 0.0500 3.2391
## 4 49.4775 nan 0.0500 3.1181
## 5 46.8498 nan 0.0500 2.5629
## 6 44.3032 nan 0.0500 2.3166
## 7 41.9600 nan 0.0500 1.9276
## 8 39.6646 nan 0.0500 2.2852
## 9 37.5780 nan 0.0500 2.0908
## 10 35.7240 nan 0.0500 1.7903
## 20 22.5620 nan 0.0500 0.9948
## 40 11.7572 nan 0.0500 0.2593
## 60 7.6888 nan 0.0500 0.1064
## 80 6.0366 nan 0.0500 0.0430
## 100 5.2608 nan 0.0500 -0.0022
## 120 4.8910 nan 0.0500 -0.0164
## 140 4.7140 nan 0.0500 -0.0105
## 160 4.6063 nan 0.0500 -0.0039
## 180 4.5044 nan 0.0500 -0.0144
## 200 4.4191 nan 0.0500 -0.0043
## 220 4.3510 nan 0.0500 -0.0239
## 240 4.2914 nan 0.0500 -0.0222
## 260 4.2361 nan 0.0500 -0.0179
## 280 4.1922 nan 0.0500 -0.0030
## 300 4.1440 nan 0.0500 -0.0071
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 57.6101 nan 0.0500 4.6628
## 2 53.2366 nan 0.0500 4.5087
## 3 49.2002 nan 0.0500 4.5266
## 4 45.3188 nan 0.0500 3.6167
## 5 42.0193 nan 0.0500 3.6309
## 6 39.0588 nan 0.0500 3.1434
## 7 35.9386 nan 0.0500 2.6117
## 8 33.3803 nan 0.0500 2.4874
## 9 30.7484 nan 0.0500 2.6640
## 10 28.6123 nan 0.0500 1.9950
## 20 14.7083 nan 0.0500 0.8919
## 40 6.2055 nan 0.0500 0.1508
## 60 4.2665 nan 0.0500 0.0072
## 80 3.5928 nan 0.0500 -0.0036
## 100 3.3012 nan 0.0500 -0.0211
## 120 3.0554 nan 0.0500 -0.0101
## 140 2.8697 nan 0.0500 -0.0080
## 160 2.6751 nan 0.0500 0.0018
## 180 2.5134 nan 0.0500 -0.0095
## 200 2.3650 nan 0.0500 -0.0143
## 220 2.2550 nan 0.0500 -0.0048
## 240 2.1278 nan 0.0500 -0.0187
## 260 2.0159 nan 0.0500 -0.0129
## 280 1.9232 nan 0.0500 -0.0215
## 300 1.8330 nan 0.0500 -0.0050
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 57.7043 nan 0.0500 5.4844
## 2 53.0352 nan 0.0500 4.7683
## 3 48.7327 nan 0.0500 4.1390
## 4 44.8769 nan 0.0500 3.9403
## 5 41.5176 nan 0.0500 3.4863
## 6 38.3935 nan 0.0500 2.6453
## 7 35.6388 nan 0.0500 2.7774
## 8 33.0377 nan 0.0500 2.6318
## 9 30.5692 nan 0.0500 2.2881
## 10 28.3643 nan 0.0500 2.0766
## 20 14.6763 nan 0.0500 0.8302
## 40 6.1216 nan 0.0500 0.1411
## 60 4.2930 nan 0.0500 0.0187
## 80 3.7666 nan 0.0500 -0.0051
## 100 3.4912 nan 0.0500 -0.0239
## 120 3.2857 nan 0.0500 -0.0090
## 140 3.1191 nan 0.0500 -0.0111
## 160 2.9533 nan 0.0500 -0.0189
## 180 2.8280 nan 0.0500 -0.0113
## 200 2.7362 nan 0.0500 -0.0128
## 220 2.6297 nan 0.0500 -0.0116
## 240 2.5261 nan 0.0500 -0.0172
## 260 2.4411 nan 0.0500 -0.0207
## 280 2.3411 nan 0.0500 -0.0067
## 300 2.2573 nan 0.0500 -0.0244
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 57.5689 nan 0.0500 4.5040
## 2 52.8280 nan 0.0500 4.4066
## 3 48.7064 nan 0.0500 4.4652
## 4 44.8798 nan 0.0500 3.5547
## 5 41.3601 nan 0.0500 3.4924
## 6 38.1744 nan 0.0500 3.1494
## 7 35.3853 nan 0.0500 2.6172
## 8 32.7987 nan 0.0500 2.4956
## 9 30.4805 nan 0.0500 2.1042
## 10 28.3960 nan 0.0500 2.0829
## 20 14.7823 nan 0.0500 0.7540
## 40 6.3443 nan 0.0500 0.1509
## 60 4.5748 nan 0.0500 0.0215
## 80 4.0581 nan 0.0500 -0.0097
## 100 3.7625 nan 0.0500 -0.0099
## 120 3.5690 nan 0.0500 -0.0071
## 140 3.4191 nan 0.0500 -0.0278
## 160 3.2920 nan 0.0500 -0.0087
## 180 3.1607 nan 0.0500 -0.0149
## 200 3.0388 nan 0.0500 -0.0067
## 220 2.9503 nan 0.0500 -0.0075
## 240 2.8705 nan 0.0500 -0.0194
## 260 2.7842 nan 0.0500 -0.0290
## 280 2.7023 nan 0.0500 -0.0095
## 300 2.6160 nan 0.0500 -0.0091
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 57.6070 nan 0.0500 5.5350
## 2 52.9644 nan 0.0500 4.4937
## 3 48.6983 nan 0.0500 4.6190
## 4 44.3573 nan 0.0500 3.6627
## 5 40.5895 nan 0.0500 4.0002
## 6 37.0560 nan 0.0500 3.2714
## 7 34.1248 nan 0.0500 3.2003
## 8 31.3187 nan 0.0500 2.5264
## 9 28.9119 nan 0.0500 2.4922
## 10 26.6396 nan 0.0500 2.1793
## 20 12.8245 nan 0.0500 0.8007
## 40 5.0838 nan 0.0500 0.0937
## 60 3.4329 nan 0.0500 0.0155
## 80 2.9072 nan 0.0500 -0.0065
## 100 2.5530 nan 0.0500 -0.0492
## 120 2.3286 nan 0.0500 -0.0274
## 140 2.1165 nan 0.0500 -0.0093
## 160 1.9288 nan 0.0500 -0.0171
## 180 1.7452 nan 0.0500 -0.0105
## 200 1.6083 nan 0.0500 -0.0147
## 220 1.4810 nan 0.0500 -0.0068
## 240 1.3548 nan 0.0500 -0.0130
## 260 1.2571 nan 0.0500 -0.0107
## 280 1.1736 nan 0.0500 -0.0046
## 300 1.0998 nan 0.0500 -0.0045
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 57.6074 nan 0.0500 5.0928
## 2 52.6480 nan 0.0500 4.9163
## 3 48.2102 nan 0.0500 3.6347
## 4 44.2609 nan 0.0500 4.1666
## 5 40.3370 nan 0.0500 3.6530
## 6 37.0098 nan 0.0500 3.0094
## 7 34.0725 nan 0.0500 3.0988
## 8 31.3034 nan 0.0500 2.5954
## 9 28.7670 nan 0.0500 2.3194
## 10 26.5441 nan 0.0500 2.1188
## 20 12.7643 nan 0.0500 0.7245
## 40 5.2802 nan 0.0500 0.0658
## 60 3.7291 nan 0.0500 0.0021
## 80 3.2486 nan 0.0500 -0.0042
## 100 2.9757 nan 0.0500 -0.0204
## 120 2.7167 nan 0.0500 -0.0158
## 140 2.5299 nan 0.0500 -0.0166
## 160 2.3685 nan 0.0500 -0.0202
## 180 2.2391 nan 0.0500 -0.0166
## 200 2.1204 nan 0.0500 -0.0182
## 220 1.9995 nan 0.0500 -0.0183
## 240 1.8869 nan 0.0500 -0.0239
## 260 1.7872 nan 0.0500 -0.0191
## 280 1.7016 nan 0.0500 -0.0199
## 300 1.6129 nan 0.0500 -0.0161
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 57.3179 nan 0.0500 5.0482
## 2 52.6618 nan 0.0500 4.7157
## 3 48.3907 nan 0.0500 4.2879
## 4 44.5964 nan 0.0500 4.0063
## 5 41.2366 nan 0.0500 3.9352
## 6 37.9516 nan 0.0500 3.0408
## 7 34.9277 nan 0.0500 2.7338
## 8 32.2526 nan 0.0500 2.7502
## 9 29.7876 nan 0.0500 2.3907
## 10 27.4730 nan 0.0500 2.3297
## 20 13.5299 nan 0.0500 0.7158
## 40 5.6291 nan 0.0500 0.0923
## 60 4.2428 nan 0.0500 0.0166
## 80 3.7190 nan 0.0500 -0.0266
## 100 3.4311 nan 0.0500 -0.0135
## 120 3.2119 nan 0.0500 -0.0191
## 140 3.0013 nan 0.0500 -0.0179
## 160 2.8499 nan 0.0500 -0.0149
## 180 2.6918 nan 0.0500 -0.0134
## 200 2.5854 nan 0.0500 -0.0091
## 220 2.4537 nan 0.0500 -0.0169
## 240 2.3694 nan 0.0500 -0.0126
## 260 2.2707 nan 0.0500 -0.0095
## 280 2.1794 nan 0.0500 -0.0179
## 300 2.0977 nan 0.0500 -0.0161
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 55.0555 nan 0.1000 7.6138
## 2 49.4053 nan 0.1000 6.6276
## 3 44.3756 nan 0.1000 4.9929
## 4 39.5129 nan 0.1000 4.2009
## 5 35.6855 nan 0.1000 3.3459
## 6 31.8681 nan 0.1000 3.3389
## 7 29.1277 nan 0.1000 2.3886
## 8 26.5008 nan 0.1000 2.8212
## 9 24.3133 nan 0.1000 2.3781
## 10 22.4860 nan 0.1000 1.9166
## 20 11.6166 nan 0.1000 0.4115
## 40 5.7238 nan 0.1000 0.0428
## 60 4.5947 nan 0.1000 -0.0107
## 80 4.3573 nan 0.1000 -0.0302
## 100 4.1669 nan 0.1000 -0.0328
## 120 4.0450 nan 0.1000 -0.0275
## 140 3.9471 nan 0.1000 -0.0277
## 160 3.8459 nan 0.1000 -0.0134
## 180 3.7614 nan 0.1000 -0.0474
## 200 3.6922 nan 0.1000 -0.0321
## 220 3.6113 nan 0.1000 -0.0294
## 240 3.5395 nan 0.1000 -0.0268
## 260 3.4978 nan 0.1000 -0.0151
## 280 3.4414 nan 0.1000 -0.0290
## 300 3.3800 nan 0.1000 -0.0228
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 55.8302 nan 0.1000 7.7743
## 2 49.2898 nan 0.1000 6.5261
## 3 43.6756 nan 0.1000 5.5281
## 4 38.9161 nan 0.1000 4.2792
## 5 35.2476 nan 0.1000 3.5644
## 6 31.9980 nan 0.1000 3.3527
## 7 29.1925 nan 0.1000 2.9808
## 8 26.6720 nan 0.1000 2.2059
## 9 24.4612 nan 0.1000 2.3958
## 10 22.3754 nan 0.1000 1.9165
## 20 11.4983 nan 0.1000 0.5774
## 40 5.7692 nan 0.1000 0.0864
## 60 4.6802 nan 0.1000 -0.0218
## 80 4.3781 nan 0.1000 -0.0262
## 100 4.2141 nan 0.1000 -0.0211
## 120 4.0957 nan 0.1000 -0.0234
## 140 4.0209 nan 0.1000 -0.0118
## 160 3.9366 nan 0.1000 -0.0429
## 180 3.8693 nan 0.1000 -0.0056
## 200 3.8009 nan 0.1000 -0.0598
## 220 3.7237 nan 0.1000 -0.0110
## 240 3.6610 nan 0.1000 -0.0210
## 260 3.5977 nan 0.1000 -0.0242
## 280 3.5416 nan 0.1000 -0.0202
## 300 3.4865 nan 0.1000 -0.0263
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 55.1560 nan 0.1000 7.5652
## 2 48.7478 nan 0.1000 7.1845
## 3 44.1969 nan 0.1000 5.0202
## 4 39.5508 nan 0.1000 4.1347
## 5 35.6722 nan 0.1000 4.4838
## 6 32.5038 nan 0.1000 3.4300
## 7 29.6769 nan 0.1000 2.8936
## 8 27.1966 nan 0.1000 2.4818
## 9 24.8045 nan 0.1000 2.2810
## 10 23.0550 nan 0.1000 1.4913
## 20 11.6762 nan 0.1000 0.6798
## 40 6.1148 nan 0.1000 0.0010
## 60 5.0670 nan 0.1000 0.0180
## 80 4.8137 nan 0.1000 -0.0086
## 100 4.6319 nan 0.1000 -0.0072
## 120 4.4658 nan 0.1000 -0.0142
## 140 4.3333 nan 0.1000 -0.0153
## 160 4.2019 nan 0.1000 -0.0058
## 180 4.1027 nan 0.1000 -0.0075
## 200 4.0159 nan 0.1000 -0.0067
## 220 3.9518 nan 0.1000 -0.0037
## 240 3.8675 nan 0.1000 -0.0124
## 260 3.8004 nan 0.1000 -0.0308
## 280 3.7408 nan 0.1000 -0.0082
## 300 3.6883 nan 0.1000 -0.0310
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 53.0963 nan 0.1000 9.8866
## 2 45.0318 nan 0.1000 7.6146
## 3 38.3788 nan 0.1000 6.0393
## 4 32.5579 nan 0.1000 5.8196
## 5 28.3663 nan 0.1000 4.4236
## 6 24.5766 nan 0.1000 3.4699
## 7 21.2377 nan 0.1000 2.9415
## 8 18.5014 nan 0.1000 2.6313
## 9 16.2677 nan 0.1000 2.1613
## 10 14.5832 nan 0.1000 1.6213
## 20 6.1299 nan 0.1000 0.2781
## 40 3.6978 nan 0.1000 -0.0354
## 60 3.1180 nan 0.1000 -0.0488
## 80 2.7482 nan 0.1000 -0.0412
## 100 2.4495 nan 0.1000 -0.0057
## 120 2.2052 nan 0.1000 -0.0500
## 140 2.0275 nan 0.1000 -0.0195
## 160 1.8484 nan 0.1000 -0.0145
## 180 1.6781 nan 0.1000 -0.0089
## 200 1.5636 nan 0.1000 -0.0083
## 220 1.4625 nan 0.1000 -0.0095
## 240 1.3620 nan 0.1000 -0.0195
## 260 1.2561 nan 0.1000 -0.0181
## 280 1.1577 nan 0.1000 -0.0176
## 300 1.0865 nan 0.1000 -0.0142
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 53.4982 nan 0.1000 9.9704
## 2 45.4958 nan 0.1000 8.1663
## 3 38.8439 nan 0.1000 6.8676
## 4 33.1167 nan 0.1000 5.0847
## 5 28.6498 nan 0.1000 5.0926
## 6 24.9079 nan 0.1000 3.6444
## 7 21.5416 nan 0.1000 3.4117
## 8 18.8026 nan 0.1000 2.5982
## 9 16.4677 nan 0.1000 2.2438
## 10 14.4618 nan 0.1000 1.6878
## 20 6.2608 nan 0.1000 0.1583
## 40 3.9947 nan 0.1000 0.0048
## 60 3.4532 nan 0.1000 -0.0695
## 80 3.0753 nan 0.1000 -0.0239
## 100 2.8015 nan 0.1000 -0.0292
## 120 2.6091 nan 0.1000 -0.0193
## 140 2.4383 nan 0.1000 -0.0359
## 160 2.2716 nan 0.1000 -0.0380
## 180 2.1292 nan 0.1000 -0.0148
## 200 2.0087 nan 0.1000 -0.0186
## 220 1.8971 nan 0.1000 -0.0416
## 240 1.7802 nan 0.1000 -0.0417
## 260 1.6889 nan 0.1000 -0.0367
## 280 1.6110 nan 0.1000 -0.0245
## 300 1.5485 nan 0.1000 -0.0115
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 52.9706 nan 0.1000 8.4499
## 2 44.8951 nan 0.1000 7.8972
## 3 38.5061 nan 0.1000 6.6447
## 4 32.9478 nan 0.1000 5.5577
## 5 28.5628 nan 0.1000 4.6357
## 6 24.6281 nan 0.1000 4.3533
## 7 21.4555 nan 0.1000 2.9754
## 8 18.7108 nan 0.1000 2.6593
## 9 16.3322 nan 0.1000 2.0587
## 10 14.7445 nan 0.1000 1.3797
## 20 6.5646 nan 0.1000 0.2890
## 40 4.2031 nan 0.1000 0.0166
## 60 3.7148 nan 0.1000 -0.0206
## 80 3.3714 nan 0.1000 -0.0672
## 100 3.1552 nan 0.1000 -0.0106
## 120 2.9588 nan 0.1000 -0.0278
## 140 2.7862 nan 0.1000 -0.0357
## 160 2.6385 nan 0.1000 -0.0402
## 180 2.4985 nan 0.1000 -0.0109
## 200 2.3784 nan 0.1000 -0.0173
## 220 2.2683 nan 0.1000 -0.0245
## 240 2.1426 nan 0.1000 -0.0382
## 260 2.0659 nan 0.1000 -0.0299
## 280 1.9689 nan 0.1000 -0.0278
## 300 1.8860 nan 0.1000 -0.0281
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 52.4231 nan 0.1000 10.4776
## 2 43.6174 nan 0.1000 8.8853
## 3 36.5281 nan 0.1000 6.8172
## 4 30.3330 nan 0.1000 6.1187
## 5 25.7279 nan 0.1000 4.5879
## 6 21.7777 nan 0.1000 3.9557
## 7 18.6932 nan 0.1000 2.8688
## 8 16.2063 nan 0.1000 2.3279
## 9 14.1763 nan 0.1000 2.1501
## 10 12.4376 nan 0.1000 1.5747
## 20 5.0274 nan 0.1000 0.1613
## 40 2.9364 nan 0.1000 -0.0381
## 60 2.3085 nan 0.1000 -0.0215
## 80 1.9718 nan 0.1000 -0.0501
## 100 1.6574 nan 0.1000 -0.0200
## 120 1.4683 nan 0.1000 -0.0246
## 140 1.2600 nan 0.1000 -0.0235
## 160 1.1154 nan 0.1000 -0.0274
## 180 0.9772 nan 0.1000 -0.0108
## 200 0.8796 nan 0.1000 -0.0109
## 220 0.7815 nan 0.1000 -0.0122
## 240 0.6943 nan 0.1000 -0.0106
## 260 0.6313 nan 0.1000 -0.0111
## 280 0.5660 nan 0.1000 -0.0107
## 300 0.5065 nan 0.1000 -0.0150
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 52.4970 nan 0.1000 10.8697
## 2 44.0576 nan 0.1000 9.3068
## 3 36.8649 nan 0.1000 6.4076
## 4 31.0434 nan 0.1000 5.0661
## 5 26.1874 nan 0.1000 4.9966
## 6 22.3797 nan 0.1000 3.8592
## 7 19.1280 nan 0.1000 2.8584
## 8 16.6271 nan 0.1000 2.2757
## 9 14.4346 nan 0.1000 2.4008
## 10 12.5175 nan 0.1000 1.8723
## 20 5.1001 nan 0.1000 0.1752
## 40 3.2941 nan 0.1000 -0.0273
## 60 2.7400 nan 0.1000 -0.0285
## 80 2.4257 nan 0.1000 -0.0495
## 100 2.1519 nan 0.1000 -0.0354
## 120 1.9314 nan 0.1000 -0.0213
## 140 1.7634 nan 0.1000 -0.0355
## 160 1.6108 nan 0.1000 -0.0276
## 180 1.4476 nan 0.1000 -0.0052
## 200 1.3215 nan 0.1000 -0.0201
## 220 1.2010 nan 0.1000 -0.0115
## 240 1.1009 nan 0.1000 -0.0314
## 260 1.0174 nan 0.1000 -0.0207
## 280 0.9310 nan 0.1000 -0.0099
## 300 0.8544 nan 0.1000 -0.0157
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 52.8931 nan 0.1000 10.7929
## 2 44.0418 nan 0.1000 8.6762
## 3 37.1766 nan 0.1000 6.8882
## 4 31.2344 nan 0.1000 6.0162
## 5 26.8118 nan 0.1000 4.1608
## 6 22.8499 nan 0.1000 3.4959
## 7 19.5087 nan 0.1000 3.1667
## 8 16.7145 nan 0.1000 2.4238
## 9 14.4300 nan 0.1000 1.8385
## 10 12.6616 nan 0.1000 1.7262
## 20 5.6587 nan 0.1000 0.1290
## 40 3.6989 nan 0.1000 -0.0200
## 60 3.2331 nan 0.1000 -0.0273
## 80 2.8993 nan 0.1000 -0.0444
## 100 2.6459 nan 0.1000 -0.0500
## 120 2.4334 nan 0.1000 -0.0359
## 140 2.2422 nan 0.1000 -0.0146
## 160 2.1009 nan 0.1000 -0.0271
## 180 1.9614 nan 0.1000 -0.0454
## 200 1.8339 nan 0.1000 -0.0387
## 220 1.7056 nan 0.1000 -0.0175
## 240 1.6171 nan 0.1000 -0.0167
## 260 1.5325 nan 0.1000 -0.0261
## 280 1.4494 nan 0.1000 -0.0204
## 300 1.3485 nan 0.1000 -0.0152
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 60.0136 nan 0.0100 0.7309
## 2 59.3069 nan 0.0100 0.7382
## 3 58.5576 nan 0.0100 0.7789
## 4 57.8037 nan 0.0100 0.7113
## 5 57.0877 nan 0.0100 0.7058
## 6 56.4809 nan 0.0100 0.6428
## 7 55.7992 nan 0.0100 0.6837
## 8 55.1057 nan 0.0100 0.6259
## 9 54.4941 nan 0.0100 0.6791
## 10 53.8237 nan 0.0100 0.6243
## 20 47.9664 nan 0.0100 0.5467
## 40 38.8344 nan 0.0100 0.3942
## 60 31.6753 nan 0.0100 0.2936
## 80 26.3466 nan 0.0100 0.2201
## 100 22.3660 nan 0.0100 0.1732
## 120 19.1525 nan 0.0100 0.1382
## 140 16.6830 nan 0.0100 0.0901
## 160 14.6963 nan 0.0100 0.0965
## 180 13.0591 nan 0.0100 0.0644
## 200 11.6676 nan 0.0100 0.0577
## 220 10.5019 nan 0.0100 0.0409
## 240 9.5397 nan 0.0100 0.0406
## 260 8.7367 nan 0.0100 0.0311
## 280 8.0795 nan 0.0100 0.0274
## 300 7.5040 nan 0.0100 0.0239
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 59.9436 nan 0.0100 0.7622
## 2 59.2310 nan 0.0100 0.7166
## 3 58.5180 nan 0.0100 0.7284
## 4 57.7595 nan 0.0100 0.7294
## 5 57.0582 nan 0.0100 0.7101
## 6 56.3601 nan 0.0100 0.6717
## 7 55.6781 nan 0.0100 0.6456
## 8 55.0569 nan 0.0100 0.6686
## 9 54.3782 nan 0.0100 0.6809
## 10 53.6954 nan 0.0100 0.6122
## 20 47.9674 nan 0.0100 0.4422
## 40 38.5254 nan 0.0100 0.4304
## 60 31.7045 nan 0.0100 0.3010
## 80 26.4296 nan 0.0100 0.2286
## 100 22.2645 nan 0.0100 0.1682
## 120 19.1053 nan 0.0100 0.1409
## 140 16.5966 nan 0.0100 0.1006
## 160 14.5532 nan 0.0100 0.0828
## 180 12.9567 nan 0.0100 0.0750
## 200 11.5875 nan 0.0100 0.0470
## 220 10.4623 nan 0.0100 0.0404
## 240 9.5353 nan 0.0100 0.0332
## 260 8.7254 nan 0.0100 0.0305
## 280 8.0461 nan 0.0100 0.0207
## 300 7.4677 nan 0.0100 0.0259
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 60.0122 nan 0.0100 0.7683
## 2 59.2449 nan 0.0100 0.6982
## 3 58.5086 nan 0.0100 0.6877
## 4 57.8015 nan 0.0100 0.7082
## 5 57.0913 nan 0.0100 0.6731
## 6 56.3931 nan 0.0100 0.6994
## 7 55.6996 nan 0.0100 0.6350
## 8 54.9988 nan 0.0100 0.6720
## 9 54.3109 nan 0.0100 0.6354
## 10 53.7220 nan 0.0100 0.6084
## 20 47.7270 nan 0.0100 0.5410
## 40 38.4607 nan 0.0100 0.3610
## 60 31.3869 nan 0.0100 0.2680
## 80 26.0707 nan 0.0100 0.2191
## 100 21.9812 nan 0.0100 0.1612
## 120 18.8080 nan 0.0100 0.1231
## 140 16.3560 nan 0.0100 0.0885
## 160 14.4122 nan 0.0100 0.0688
## 180 12.8133 nan 0.0100 0.0640
## 200 11.5218 nan 0.0100 0.0530
## 220 10.4511 nan 0.0100 0.0348
## 240 9.5459 nan 0.0100 0.0383
## 260 8.7682 nan 0.0100 0.0243
## 280 8.0921 nan 0.0100 0.0221
## 300 7.5235 nan 0.0100 0.0240
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 59.7489 nan 0.0100 1.0797
## 2 58.7624 nan 0.0100 0.9633
## 3 57.8081 nan 0.0100 0.9259
## 4 56.8097 nan 0.0100 0.9561
## 5 55.8406 nan 0.0100 0.9998
## 6 54.8998 nan 0.0100 0.9254
## 7 54.0152 nan 0.0100 0.8577
## 8 53.1138 nan 0.0100 0.8383
## 9 52.2625 nan 0.0100 0.8395
## 10 51.4682 nan 0.0100 0.8769
## 20 43.8430 nan 0.0100 0.6853
## 40 32.4891 nan 0.0100 0.4373
## 60 24.3596 nan 0.0100 0.3014
## 80 18.7091 nan 0.0100 0.2070
## 100 14.7317 nan 0.0100 0.1750
## 120 11.8547 nan 0.0100 0.1179
## 140 9.7365 nan 0.0100 0.0670
## 160 8.1844 nan 0.0100 0.0504
## 180 7.0230 nan 0.0100 0.0427
## 200 6.1941 nan 0.0100 0.0238
## 220 5.5471 nan 0.0100 0.0225
## 240 5.0435 nan 0.0100 0.0177
## 260 4.6756 nan 0.0100 0.0100
## 280 4.3899 nan 0.0100 0.0063
## 300 4.1639 nan 0.0100 0.0040
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 59.7124 nan 0.0100 0.8964
## 2 58.7423 nan 0.0100 0.9351
## 3 57.7704 nan 0.0100 0.9712
## 4 56.8275 nan 0.0100 0.9298
## 5 55.9085 nan 0.0100 0.9019
## 6 54.9836 nan 0.0100 0.9378
## 7 54.1080 nan 0.0100 0.9011
## 8 53.2389 nan 0.0100 0.8544
## 9 52.3379 nan 0.0100 0.9102
## 10 51.4581 nan 0.0100 0.8512
## 20 43.8869 nan 0.0100 0.6327
## 40 32.3847 nan 0.0100 0.4783
## 60 24.4073 nan 0.0100 0.2961
## 80 18.7973 nan 0.0100 0.2407
## 100 14.8049 nan 0.0100 0.1603
## 120 11.9158 nan 0.0100 0.1197
## 140 9.8633 nan 0.0100 0.0725
## 160 8.3504 nan 0.0100 0.0476
## 180 7.2150 nan 0.0100 0.0409
## 200 6.3375 nan 0.0100 0.0353
## 220 5.7002 nan 0.0100 0.0194
## 240 5.2224 nan 0.0100 0.0113
## 260 4.8668 nan 0.0100 0.0112
## 280 4.5955 nan 0.0100 0.0077
## 300 4.3983 nan 0.0100 0.0019
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 59.7504 nan 0.0100 0.9070
## 2 58.7920 nan 0.0100 0.8851
## 3 57.8570 nan 0.0100 0.9714
## 4 56.9265 nan 0.0100 0.9617
## 5 56.0434 nan 0.0100 0.9323
## 6 55.1533 nan 0.0100 0.9494
## 7 54.3481 nan 0.0100 0.9272
## 8 53.4239 nan 0.0100 0.8740
## 9 52.5853 nan 0.0100 0.8910
## 10 51.7667 nan 0.0100 0.8426
## 20 44.1633 nan 0.0100 0.7738
## 40 32.4893 nan 0.0100 0.4694
## 60 24.5213 nan 0.0100 0.3394
## 80 18.9678 nan 0.0100 0.2050
## 100 14.9668 nan 0.0100 0.1445
## 120 12.1689 nan 0.0100 0.1153
## 140 10.0862 nan 0.0100 0.0811
## 160 8.5776 nan 0.0100 0.0544
## 180 7.4089 nan 0.0100 0.0373
## 200 6.5669 nan 0.0100 0.0363
## 220 5.9359 nan 0.0100 0.0219
## 240 5.4538 nan 0.0100 0.0095
## 260 5.0953 nan 0.0100 0.0062
## 280 4.8104 nan 0.0100 0.0027
## 300 4.6044 nan 0.0100 -0.0001
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 59.7258 nan 0.0100 1.0260
## 2 58.6768 nan 0.0100 1.0375
## 3 57.6881 nan 0.0100 0.9698
## 4 56.6849 nan 0.0100 1.0019
## 5 55.6817 nan 0.0100 1.0072
## 6 54.7410 nan 0.0100 1.0024
## 7 53.7879 nan 0.0100 0.9439
## 8 52.8675 nan 0.0100 0.9711
## 9 51.9600 nan 0.0100 0.8284
## 10 51.0695 nan 0.0100 0.8600
## 20 43.0478 nan 0.0100 0.6777
## 40 30.9084 nan 0.0100 0.4363
## 60 22.6216 nan 0.0100 0.2841
## 80 16.9050 nan 0.0100 0.2333
## 100 12.9873 nan 0.0100 0.1665
## 120 10.2681 nan 0.0100 0.1101
## 140 8.2704 nan 0.0100 0.0788
## 160 6.8515 nan 0.0100 0.0613
## 180 5.8686 nan 0.0100 0.0225
## 200 5.1360 nan 0.0100 0.0168
## 220 4.6025 nan 0.0100 0.0188
## 240 4.2339 nan 0.0100 0.0094
## 260 3.9213 nan 0.0100 0.0007
## 280 3.6690 nan 0.0100 0.0025
## 300 3.4709 nan 0.0100 -0.0011
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 59.6912 nan 0.0100 1.1606
## 2 58.6333 nan 0.0100 0.9028
## 3 57.6481 nan 0.0100 0.9227
## 4 56.6834 nan 0.0100 0.9183
## 5 55.7213 nan 0.0100 0.9961
## 6 54.7459 nan 0.0100 0.9148
## 7 53.8130 nan 0.0100 0.8938
## 8 52.8851 nan 0.0100 0.8005
## 9 51.9532 nan 0.0100 0.8151
## 10 51.0693 nan 0.0100 0.8338
## 20 42.9955 nan 0.0100 0.6778
## 40 31.0907 nan 0.0100 0.4672
## 60 22.7488 nan 0.0100 0.3630
## 80 17.0070 nan 0.0100 0.2167
## 100 13.1385 nan 0.0100 0.1596
## 120 10.3236 nan 0.0100 0.1102
## 140 8.4081 nan 0.0100 0.0779
## 160 7.0312 nan 0.0100 0.0479
## 180 6.0379 nan 0.0100 0.0314
## 200 5.3245 nan 0.0100 0.0155
## 220 4.7935 nan 0.0100 0.0173
## 240 4.4352 nan 0.0100 0.0070
## 260 4.1571 nan 0.0100 0.0076
## 280 3.9369 nan 0.0100 0.0057
## 300 3.7738 nan 0.0100 0.0021
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 59.7164 nan 0.0100 1.0220
## 2 58.7366 nan 0.0100 0.9292
## 3 57.7222 nan 0.0100 0.9462
## 4 56.6980 nan 0.0100 0.9495
## 5 55.7850 nan 0.0100 0.9422
## 6 54.8432 nan 0.0100 0.8777
## 7 53.9257 nan 0.0100 1.0183
## 8 52.9730 nan 0.0100 0.9172
## 9 52.0858 nan 0.0100 0.8241
## 10 51.1973 nan 0.0100 0.9727
## 20 43.2499 nan 0.0100 0.6815
## 40 31.2662 nan 0.0100 0.4994
## 60 23.0804 nan 0.0100 0.3214
## 80 17.3888 nan 0.0100 0.2367
## 100 13.4122 nan 0.0100 0.1580
## 120 10.6160 nan 0.0100 0.1278
## 140 8.7383 nan 0.0100 0.0649
## 160 7.3541 nan 0.0100 0.0549
## 180 6.3414 nan 0.0100 0.0258
## 200 5.6619 nan 0.0100 0.0221
## 220 5.1349 nan 0.0100 0.0153
## 240 4.7506 nan 0.0100 0.0090
## 260 4.4798 nan 0.0100 0.0064
## 280 4.2722 nan 0.0100 0.0031
## 300 4.1111 nan 0.0100 0.0005
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 57.0708 nan 0.0500 3.4261
## 2 53.4832 nan 0.0500 3.5470
## 3 50.2492 nan 0.0500 2.9251
## 4 47.4588 nan 0.0500 2.7320
## 5 44.6253 nan 0.0500 2.3892
## 6 42.0264 nan 0.0500 2.2837
## 7 39.6164 nan 0.0500 2.2924
## 8 37.5523 nan 0.0500 1.8032
## 9 35.5230 nan 0.0500 1.7495
## 10 33.7832 nan 0.0500 1.7217
## 20 21.4582 nan 0.0500 0.7238
## 40 11.1680 nan 0.0500 0.3059
## 60 7.2943 nan 0.0500 0.0969
## 80 5.5371 nan 0.0500 0.0298
## 100 4.7182 nan 0.0500 0.0174
## 120 4.2944 nan 0.0500 -0.0006
## 140 4.0854 nan 0.0500 0.0033
## 160 3.9731 nan 0.0500 0.0011
## 180 3.9094 nan 0.0500 -0.0373
## 200 3.8431 nan 0.0500 0.0023
## 220 3.7720 nan 0.0500 -0.0119
## 240 3.7190 nan 0.0500 -0.0093
## 260 3.6729 nan 0.0500 -0.0129
## 280 3.6199 nan 0.0500 -0.0257
## 300 3.5688 nan 0.0500 -0.0191
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 56.9279 nan 0.0500 3.5968
## 2 53.2888 nan 0.0500 3.3480
## 3 50.0576 nan 0.0500 2.6050
## 4 47.4279 nan 0.0500 2.7749
## 5 44.9536 nan 0.0500 2.2471
## 6 42.6819 nan 0.0500 2.3080
## 7 40.6400 nan 0.0500 2.2747
## 8 38.3433 nan 0.0500 2.1405
## 9 36.4497 nan 0.0500 1.8345
## 10 34.6595 nan 0.0500 1.7560
## 20 21.9080 nan 0.0500 0.8333
## 40 11.3024 nan 0.0500 0.2667
## 60 7.3082 nan 0.0500 0.0769
## 80 5.5560 nan 0.0500 0.0589
## 100 4.7648 nan 0.0500 0.0268
## 120 4.3887 nan 0.0500 -0.0072
## 140 4.2133 nan 0.0500 -0.0085
## 160 4.0883 nan 0.0500 -0.0011
## 180 3.9976 nan 0.0500 -0.0048
## 200 3.9324 nan 0.0500 -0.0069
## 220 3.8944 nan 0.0500 -0.0134
## 240 3.8400 nan 0.0500 -0.0030
## 260 3.7719 nan 0.0500 -0.0172
## 280 3.7233 nan 0.0500 -0.0166
## 300 3.6749 nan 0.0500 -0.0062
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 56.8978 nan 0.0500 3.7894
## 2 53.5460 nan 0.0500 3.3779
## 3 50.9201 nan 0.0500 2.5757
## 4 48.0603 nan 0.0500 2.7026
## 5 45.2111 nan 0.0500 2.7884
## 6 42.6579 nan 0.0500 2.0706
## 7 40.2014 nan 0.0500 2.2035
## 8 38.0148 nan 0.0500 1.9890
## 9 36.1214 nan 0.0500 1.8842
## 10 34.1316 nan 0.0500 1.7787
## 20 21.6834 nan 0.0500 0.9314
## 40 11.1737 nan 0.0500 0.2354
## 60 7.2628 nan 0.0500 0.0347
## 80 5.5891 nan 0.0500 0.0537
## 100 4.8670 nan 0.0500 0.0175
## 120 4.5663 nan 0.0500 -0.0026
## 140 4.3998 nan 0.0500 0.0058
## 160 4.2818 nan 0.0500 -0.0066
## 180 4.2154 nan 0.0500 -0.0180
## 200 4.1390 nan 0.0500 -0.0084
## 220 4.0767 nan 0.0500 -0.0062
## 240 4.0111 nan 0.0500 -0.0099
## 260 3.9545 nan 0.0500 -0.0106
## 280 3.9060 nan 0.0500 -0.0047
## 300 3.8599 nan 0.0500 -0.0144
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 55.9425 nan 0.0500 4.4165
## 2 51.6150 nan 0.0500 4.2477
## 3 47.5709 nan 0.0500 3.8185
## 4 44.0024 nan 0.0500 3.5616
## 5 40.4063 nan 0.0500 3.2855
## 6 37.4479 nan 0.0500 2.9129
## 7 34.7746 nan 0.0500 2.9527
## 8 32.3206 nan 0.0500 2.2662
## 9 29.9910 nan 0.0500 2.2748
## 10 27.7969 nan 0.0500 2.3176
## 20 14.6070 nan 0.0500 0.7513
## 40 6.2589 nan 0.0500 0.0918
## 60 4.1484 nan 0.0500 0.0122
## 80 3.5259 nan 0.0500 -0.0068
## 100 3.1920 nan 0.0500 -0.0192
## 120 2.9503 nan 0.0500 -0.0159
## 140 2.7679 nan 0.0500 -0.0069
## 160 2.5788 nan 0.0500 -0.0014
## 180 2.4560 nan 0.0500 -0.0029
## 200 2.3124 nan 0.0500 -0.0062
## 220 2.1909 nan 0.0500 -0.0118
## 240 2.1008 nan 0.0500 -0.0078
## 260 2.0215 nan 0.0500 -0.0095
## 280 1.9458 nan 0.0500 -0.0138
## 300 1.8626 nan 0.0500 -0.0176
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 55.9850 nan 0.0500 4.9956
## 2 51.5332 nan 0.0500 4.4692
## 3 47.4913 nan 0.0500 4.3128
## 4 43.8275 nan 0.0500 3.6534
## 5 40.5928 nan 0.0500 3.5577
## 6 37.6119 nan 0.0500 3.1624
## 7 34.7157 nan 0.0500 2.4115
## 8 32.0531 nan 0.0500 2.4976
## 9 29.7861 nan 0.0500 2.2356
## 10 27.6384 nan 0.0500 1.8172
## 20 14.7108 nan 0.0500 0.6873
## 40 6.5437 nan 0.0500 0.1095
## 60 4.5010 nan 0.0500 0.0318
## 80 3.9269 nan 0.0500 -0.0094
## 100 3.5592 nan 0.0500 -0.0372
## 120 3.3482 nan 0.0500 -0.0346
## 140 3.1675 nan 0.0500 -0.0130
## 160 3.0351 nan 0.0500 -0.0165
## 180 2.9170 nan 0.0500 -0.0175
## 200 2.7937 nan 0.0500 -0.0070
## 220 2.6939 nan 0.0500 -0.0158
## 240 2.5863 nan 0.0500 -0.0158
## 260 2.4855 nan 0.0500 -0.0146
## 280 2.3889 nan 0.0500 -0.0096
## 300 2.3010 nan 0.0500 -0.0093
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 55.7401 nan 0.0500 4.9635
## 2 51.3754 nan 0.0500 4.7747
## 3 47.3313 nan 0.0500 4.1007
## 4 43.5089 nan 0.0500 3.7418
## 5 39.8664 nan 0.0500 3.4419
## 6 36.7988 nan 0.0500 2.9628
## 7 33.9349 nan 0.0500 2.6719
## 8 31.3412 nan 0.0500 2.4528
## 9 29.0799 nan 0.0500 2.1439
## 10 27.0874 nan 0.0500 2.1597
## 20 14.5006 nan 0.0500 0.8585
## 40 6.5885 nan 0.0500 0.1377
## 60 4.6493 nan 0.0500 -0.0094
## 80 4.1056 nan 0.0500 -0.0054
## 100 3.8363 nan 0.0500 -0.0279
## 120 3.6283 nan 0.0500 -0.0136
## 140 3.4383 nan 0.0500 -0.0174
## 160 3.3067 nan 0.0500 -0.0139
## 180 3.1766 nan 0.0500 -0.0075
## 200 3.0857 nan 0.0500 -0.0393
## 220 2.9728 nan 0.0500 -0.0330
## 240 2.8943 nan 0.0500 -0.0329
## 260 2.8106 nan 0.0500 -0.0060
## 280 2.7267 nan 0.0500 -0.0197
## 300 2.6547 nan 0.0500 -0.0091
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 55.5695 nan 0.0500 4.8853
## 2 51.0947 nan 0.0500 4.5775
## 3 46.7267 nan 0.0500 4.6384
## 4 42.8285 nan 0.0500 3.5816
## 5 39.3509 nan 0.0500 3.3451
## 6 36.1016 nan 0.0500 2.9419
## 7 33.0778 nan 0.0500 2.7151
## 8 30.4066 nan 0.0500 2.6578
## 9 28.1277 nan 0.0500 1.8531
## 10 26.0370 nan 0.0500 2.1431
## 20 12.7724 nan 0.0500 0.6495
## 40 5.1641 nan 0.0500 0.0681
## 60 3.4869 nan 0.0500 0.0085
## 80 2.8689 nan 0.0500 -0.0127
## 100 2.5049 nan 0.0500 -0.0189
## 120 2.2382 nan 0.0500 -0.0128
## 140 2.0354 nan 0.0500 -0.0146
## 160 1.8743 nan 0.0500 -0.0186
## 180 1.7375 nan 0.0500 -0.0200
## 200 1.5886 nan 0.0500 -0.0122
## 220 1.4566 nan 0.0500 -0.0089
## 240 1.3520 nan 0.0500 -0.0075
## 260 1.2580 nan 0.0500 -0.0072
## 280 1.1705 nan 0.0500 -0.0143
## 300 1.0886 nan 0.0500 -0.0108
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 55.7056 nan 0.0500 5.2085
## 2 50.8091 nan 0.0500 4.4483
## 3 46.6120 nan 0.0500 4.1828
## 4 42.7302 nan 0.0500 4.0813
## 5 39.3565 nan 0.0500 2.9791
## 6 36.1900 nan 0.0500 2.8864
## 7 33.2310 nan 0.0500 2.5093
## 8 30.7194 nan 0.0500 2.3333
## 9 28.3601 nan 0.0500 2.2850
## 10 26.2464 nan 0.0500 2.1626
## 20 13.1996 nan 0.0500 0.7463
## 40 5.4008 nan 0.0500 0.0814
## 60 3.8696 nan 0.0500 -0.0022
## 80 3.3145 nan 0.0500 -0.0160
## 100 3.0309 nan 0.0500 -0.0381
## 120 2.8319 nan 0.0500 -0.0083
## 140 2.6408 nan 0.0500 -0.0087
## 160 2.4663 nan 0.0500 -0.0138
## 180 2.3286 nan 0.0500 -0.0124
## 200 2.1742 nan 0.0500 -0.0277
## 220 2.0430 nan 0.0500 -0.0089
## 240 1.9449 nan 0.0500 -0.0173
## 260 1.8509 nan 0.0500 -0.0158
## 280 1.7439 nan 0.0500 -0.0144
## 300 1.6656 nan 0.0500 -0.0160
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 55.7311 nan 0.0500 4.9251
## 2 51.1347 nan 0.0500 4.9513
## 3 46.8736 nan 0.0500 4.0580
## 4 43.2034 nan 0.0500 3.8862
## 5 39.5175 nan 0.0500 3.2606
## 6 36.4749 nan 0.0500 3.3231
## 7 33.6722 nan 0.0500 2.7951
## 8 31.1216 nan 0.0500 2.5245
## 9 28.9134 nan 0.0500 2.2811
## 10 26.7782 nan 0.0500 2.0307
## 20 13.2069 nan 0.0500 0.7264
## 40 5.5949 nan 0.0500 0.1197
## 60 4.1884 nan 0.0500 -0.0012
## 80 3.7078 nan 0.0500 -0.0077
## 100 3.4398 nan 0.0500 -0.0144
## 120 3.2301 nan 0.0500 -0.0226
## 140 3.0666 nan 0.0500 -0.0096
## 160 2.8969 nan 0.0500 -0.0150
## 180 2.7509 nan 0.0500 -0.0088
## 200 2.6578 nan 0.0500 -0.0292
## 220 2.5397 nan 0.0500 -0.0141
## 240 2.4359 nan 0.0500 -0.0224
## 260 2.3378 nan 0.0500 -0.0136
## 280 2.2502 nan 0.0500 -0.0277
## 300 2.1721 nan 0.0500 -0.0121
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 53.0201 nan 0.1000 6.7912
## 2 47.1671 nan 0.1000 5.6943
## 3 41.8649 nan 0.1000 4.6413
## 4 37.3717 nan 0.1000 3.9270
## 5 33.4679 nan 0.1000 3.7025
## 6 30.7213 nan 0.1000 2.6611
## 7 27.9469 nan 0.1000 2.6493
## 8 25.6977 nan 0.1000 2.1255
## 9 23.1097 nan 0.1000 1.9221
## 10 21.0413 nan 0.1000 1.6492
## 20 11.1598 nan 0.1000 0.4762
## 40 5.4967 nan 0.1000 0.0876
## 60 4.3596 nan 0.1000 -0.0277
## 80 4.0585 nan 0.1000 -0.0126
## 100 3.9124 nan 0.1000 -0.0106
## 120 3.8047 nan 0.1000 -0.0418
## 140 3.6896 nan 0.1000 -0.0394
## 160 3.5624 nan 0.1000 -0.0304
## 180 3.4733 nan 0.1000 -0.0154
## 200 3.4134 nan 0.1000 -0.0360
## 220 3.3151 nan 0.1000 0.0020
## 240 3.2590 nan 0.1000 -0.0441
## 260 3.1976 nan 0.1000 -0.0249
## 280 3.1588 nan 0.1000 -0.0069
## 300 3.0971 nan 0.1000 -0.0161
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 52.9720 nan 0.1000 7.0820
## 2 47.8281 nan 0.1000 5.5254
## 3 43.0021 nan 0.1000 4.5666
## 4 38.2168 nan 0.1000 4.3922
## 5 33.9648 nan 0.1000 3.4568
## 6 30.6128 nan 0.1000 3.2502
## 7 27.7217 nan 0.1000 2.7963
## 8 25.6289 nan 0.1000 1.8686
## 9 23.4901 nan 0.1000 1.9565
## 10 21.6803 nan 0.1000 1.6614
## 20 11.0081 nan 0.1000 0.4699
## 40 5.6507 nan 0.1000 0.1134
## 60 4.5026 nan 0.1000 0.0090
## 80 4.2631 nan 0.1000 -0.0388
## 100 4.0980 nan 0.1000 -0.0167
## 120 3.9801 nan 0.1000 -0.0244
## 140 3.8476 nan 0.1000 -0.0189
## 160 3.7643 nan 0.1000 -0.0034
## 180 3.6628 nan 0.1000 -0.0406
## 200 3.5835 nan 0.1000 -0.0117
## 220 3.5044 nan 0.1000 -0.0109
## 240 3.4446 nan 0.1000 -0.0361
## 260 3.3838 nan 0.1000 -0.0078
## 280 3.3332 nan 0.1000 -0.0151
## 300 3.2810 nan 0.1000 -0.0139
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 54.2314 nan 0.1000 5.7575
## 2 47.3634 nan 0.1000 5.7790
## 3 41.9419 nan 0.1000 5.3284
## 4 37.8054 nan 0.1000 4.3571
## 5 33.7300 nan 0.1000 3.0227
## 6 30.5260 nan 0.1000 3.4160
## 7 27.9784 nan 0.1000 2.5033
## 8 25.6028 nan 0.1000 2.3520
## 9 23.8093 nan 0.1000 1.6925
## 10 21.9359 nan 0.1000 1.8866
## 20 11.3268 nan 0.1000 0.6500
## 40 5.6763 nan 0.1000 0.1065
## 60 4.6272 nan 0.1000 0.0288
## 80 4.3596 nan 0.1000 -0.0253
## 100 4.1986 nan 0.1000 -0.0032
## 120 4.0725 nan 0.1000 -0.0292
## 140 3.9706 nan 0.1000 -0.0367
## 160 3.9017 nan 0.1000 -0.0261
## 180 3.8187 nan 0.1000 -0.0203
## 200 3.7687 nan 0.1000 -0.0258
## 220 3.6867 nan 0.1000 0.0019
## 240 3.6335 nan 0.1000 -0.0391
## 260 3.5575 nan 0.1000 -0.0036
## 280 3.4870 nan 0.1000 -0.0101
## 300 3.4292 nan 0.1000 -0.0392
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 51.6126 nan 0.1000 9.7468
## 2 43.6817 nan 0.1000 8.1560
## 3 36.8655 nan 0.1000 6.3558
## 4 31.2082 nan 0.1000 4.5009
## 5 26.5961 nan 0.1000 3.7140
## 6 22.8896 nan 0.1000 3.6687
## 7 19.6821 nan 0.1000 2.7012
## 8 17.3127 nan 0.1000 2.0202
## 9 15.3611 nan 0.1000 1.6812
## 10 13.7626 nan 0.1000 1.4007
## 20 6.0800 nan 0.1000 0.2733
## 40 3.6523 nan 0.1000 -0.0383
## 60 3.0679 nan 0.1000 -0.0400
## 80 2.6484 nan 0.1000 -0.0421
## 100 2.3701 nan 0.1000 -0.0294
## 120 2.1359 nan 0.1000 -0.0119
## 140 1.9004 nan 0.1000 -0.0120
## 160 1.7491 nan 0.1000 -0.0176
## 180 1.5818 nan 0.1000 -0.0052
## 200 1.4524 nan 0.1000 -0.0187
## 220 1.3678 nan 0.1000 -0.0142
## 240 1.2733 nan 0.1000 -0.0144
## 260 1.2029 nan 0.1000 -0.0167
## 280 1.1294 nan 0.1000 -0.0155
## 300 1.0542 nan 0.1000 -0.0157
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 51.2698 nan 0.1000 9.5994
## 2 43.8665 nan 0.1000 7.5947
## 3 37.3628 nan 0.1000 6.3916
## 4 31.9483 nan 0.1000 4.5749
## 5 27.5093 nan 0.1000 4.6154
## 6 24.0422 nan 0.1000 3.1865
## 7 20.9710 nan 0.1000 2.8786
## 8 18.5160 nan 0.1000 1.9100
## 9 16.5711 nan 0.1000 1.7528
## 10 14.8592 nan 0.1000 1.7289
## 20 6.3916 nan 0.1000 0.3117
## 40 3.9074 nan 0.1000 -0.0486
## 60 3.4775 nan 0.1000 -0.0747
## 80 3.1672 nan 0.1000 -0.0339
## 100 2.9344 nan 0.1000 -0.0468
## 120 2.7406 nan 0.1000 -0.0289
## 140 2.5548 nan 0.1000 -0.0593
## 160 2.3731 nan 0.1000 -0.0294
## 180 2.1705 nan 0.1000 -0.0301
## 200 2.0504 nan 0.1000 -0.0356
## 220 1.9382 nan 0.1000 -0.0232
## 240 1.8042 nan 0.1000 -0.0123
## 260 1.6814 nan 0.1000 -0.0280
## 280 1.5893 nan 0.1000 -0.0283
## 300 1.5040 nan 0.1000 -0.0090
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 51.1814 nan 0.1000 9.9841
## 2 43.5471 nan 0.1000 7.9245
## 3 37.1071 nan 0.1000 6.3505
## 4 31.8947 nan 0.1000 5.1912
## 5 27.5006 nan 0.1000 4.5199
## 6 23.8010 nan 0.1000 3.1701
## 7 20.7672 nan 0.1000 2.7958
## 8 18.4720 nan 0.1000 2.0899
## 9 16.3873 nan 0.1000 2.0008
## 10 14.4815 nan 0.1000 1.6456
## 20 6.5498 nan 0.1000 0.3072
## 40 4.1843 nan 0.1000 -0.0065
## 60 3.6697 nan 0.1000 -0.0241
## 80 3.4116 nan 0.1000 0.0037
## 100 3.2172 nan 0.1000 -0.0608
## 120 3.0223 nan 0.1000 -0.0571
## 140 2.8498 nan 0.1000 -0.0301
## 160 2.6892 nan 0.1000 -0.0272
## 180 2.5411 nan 0.1000 -0.0468
## 200 2.3915 nan 0.1000 -0.0205
## 220 2.3031 nan 0.1000 -0.0556
## 240 2.1949 nan 0.1000 -0.0098
## 260 2.0899 nan 0.1000 -0.0178
## 280 1.9812 nan 0.1000 -0.0209
## 300 1.9129 nan 0.1000 -0.0177
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 51.2967 nan 0.1000 10.4184
## 2 43.0139 nan 0.1000 7.6793
## 3 35.8131 nan 0.1000 6.2183
## 4 30.1520 nan 0.1000 4.5974
## 5 25.4157 nan 0.1000 5.1502
## 6 21.8744 nan 0.1000 3.7899
## 7 18.7071 nan 0.1000 3.1930
## 8 16.1025 nan 0.1000 2.6241
## 9 13.9893 nan 0.1000 1.9805
## 10 12.5347 nan 0.1000 1.5300
## 20 4.9977 nan 0.1000 0.2533
## 40 2.9033 nan 0.1000 -0.0265
## 60 2.2183 nan 0.1000 -0.0443
## 80 1.8877 nan 0.1000 -0.0629
## 100 1.5792 nan 0.1000 -0.0167
## 120 1.3367 nan 0.1000 -0.0291
## 140 1.1469 nan 0.1000 -0.0117
## 160 0.9912 nan 0.1000 -0.0103
## 180 0.8554 nan 0.1000 -0.0120
## 200 0.7589 nan 0.1000 -0.0074
## 220 0.6743 nan 0.1000 -0.0129
## 240 0.6083 nan 0.1000 -0.0085
## 260 0.5374 nan 0.1000 -0.0076
## 280 0.4791 nan 0.1000 -0.0085
## 300 0.4312 nan 0.1000 -0.0073
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 50.5501 nan 0.1000 10.3366
## 2 42.0344 nan 0.1000 8.3214
## 3 35.0822 nan 0.1000 7.2918
## 4 29.7430 nan 0.1000 5.0071
## 5 25.0099 nan 0.1000 3.8199
## 6 21.2212 nan 0.1000 3.4886
## 7 18.4426 nan 0.1000 2.4005
## 8 16.0871 nan 0.1000 2.1322
## 9 13.9715 nan 0.1000 2.1783
## 10 12.3125 nan 0.1000 1.3296
## 20 5.0131 nan 0.1000 0.1336
## 40 3.2769 nan 0.1000 -0.0559
## 60 2.7738 nan 0.1000 -0.0476
## 80 2.4876 nan 0.1000 -0.0527
## 100 2.2089 nan 0.1000 -0.0375
## 120 1.9899 nan 0.1000 -0.0262
## 140 1.8270 nan 0.1000 -0.0396
## 160 1.6009 nan 0.1000 -0.0525
## 180 1.4766 nan 0.1000 -0.0362
## 200 1.3451 nan 0.1000 -0.0142
## 220 1.2159 nan 0.1000 -0.0174
## 240 1.1201 nan 0.1000 -0.0062
## 260 1.0292 nan 0.1000 -0.0183
## 280 0.9507 nan 0.1000 -0.0123
## 300 0.8717 nan 0.1000 -0.0074
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 50.4377 nan 0.1000 10.0612
## 2 42.2709 nan 0.1000 8.2933
## 3 35.7439 nan 0.1000 6.9922
## 4 29.9256 nan 0.1000 5.6780
## 5 25.5106 nan 0.1000 3.9175
## 6 21.9648 nan 0.1000 3.3536
## 7 19.2097 nan 0.1000 2.7483
## 8 16.5308 nan 0.1000 2.3399
## 9 14.4251 nan 0.1000 1.9576
## 10 12.7110 nan 0.1000 1.8204
## 20 5.5993 nan 0.1000 0.2558
## 40 3.8591 nan 0.1000 -0.0401
## 60 3.3687 nan 0.1000 -0.0628
## 80 3.0184 nan 0.1000 -0.0439
## 100 2.7602 nan 0.1000 -0.0416
## 120 2.5498 nan 0.1000 -0.0279
## 140 2.3568 nan 0.1000 -0.0183
## 160 2.1967 nan 0.1000 -0.0228
## 180 2.0427 nan 0.1000 -0.0190
## 200 1.9039 nan 0.1000 -0.0362
## 220 1.7758 nan 0.1000 -0.0369
## 240 1.6617 nan 0.1000 -0.0217
## 260 1.5450 nan 0.1000 -0.0273
## 280 1.4692 nan 0.1000 -0.0114
## 300 1.3721 nan 0.1000 -0.0124
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 59.7802 nan 0.0100 0.7357
## 2 59.0511 nan 0.0100 0.7323
## 3 58.3037 nan 0.0100 0.6932
## 4 57.5961 nan 0.0100 0.6842
## 5 56.9363 nan 0.0100 0.6610
## 6 56.2517 nan 0.0100 0.6939
## 7 55.6626 nan 0.0100 0.6442
## 8 55.0153 nan 0.0100 0.6660
## 9 54.3390 nan 0.0100 0.6426
## 10 53.6719 nan 0.0100 0.6308
## 20 47.7516 nan 0.0100 0.5090
## 40 38.5617 nan 0.0100 0.3554
## 60 31.9074 nan 0.0100 0.2400
## 80 26.8206 nan 0.0100 0.2481
## 100 22.7279 nan 0.0100 0.1786
## 120 19.5876 nan 0.0100 0.1309
## 140 17.1873 nan 0.0100 0.1063
## 160 15.2162 nan 0.0100 0.0885
## 180 13.6377 nan 0.0100 0.0699
## 200 12.3013 nan 0.0100 0.0474
## 220 11.1336 nan 0.0100 0.0368
## 240 10.2211 nan 0.0100 0.0341
## 260 9.4366 nan 0.0100 0.0278
## 280 8.7483 nan 0.0100 0.0226
## 300 8.1385 nan 0.0100 0.0196
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 59.8481 nan 0.0100 0.7078
## 2 59.1059 nan 0.0100 0.7472
## 3 58.4287 nan 0.0100 0.7632
## 4 57.7299 nan 0.0100 0.6834
## 5 57.1300 nan 0.0100 0.6425
## 6 56.4520 nan 0.0100 0.6361
## 7 55.7836 nan 0.0100 0.6218
## 8 55.1044 nan 0.0100 0.5973
## 9 54.4765 nan 0.0100 0.6439
## 10 53.8710 nan 0.0100 0.6292
## 20 47.9486 nan 0.0100 0.5116
## 40 38.7935 nan 0.0100 0.3702
## 60 31.9034 nan 0.0100 0.2722
## 80 26.7048 nan 0.0100 0.2088
## 100 22.6756 nan 0.0100 0.1636
## 120 19.5336 nan 0.0100 0.0773
## 140 17.1238 nan 0.0100 0.1133
## 160 15.1701 nan 0.0100 0.0734
## 180 13.5280 nan 0.0100 0.0471
## 200 12.1957 nan 0.0100 0.0429
## 220 11.0711 nan 0.0100 0.0465
## 240 10.1066 nan 0.0100 0.0370
## 260 9.3002 nan 0.0100 0.0371
## 280 8.6126 nan 0.0100 0.0263
## 300 8.0320 nan 0.0100 0.0165
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 59.8285 nan 0.0100 0.7512
## 2 59.0799 nan 0.0100 0.6581
## 3 58.3778 nan 0.0100 0.7019
## 4 57.6990 nan 0.0100 0.6918
## 5 56.9748 nan 0.0100 0.7173
## 6 56.2622 nan 0.0100 0.6385
## 7 55.5478 nan 0.0100 0.6622
## 8 54.8353 nan 0.0100 0.6592
## 9 54.1843 nan 0.0100 0.6116
## 10 53.5202 nan 0.0100 0.5804
## 20 47.7961 nan 0.0100 0.4823
## 40 38.7859 nan 0.0100 0.4111
## 60 32.0661 nan 0.0100 0.2158
## 80 26.7199 nan 0.0100 0.2148
## 100 22.6224 nan 0.0100 0.1511
## 120 19.5549 nan 0.0100 0.1094
## 140 17.0971 nan 0.0100 0.0986
## 160 15.1495 nan 0.0100 0.0808
## 180 13.5290 nan 0.0100 0.0554
## 200 12.2197 nan 0.0100 0.0438
## 220 11.1515 nan 0.0100 0.0385
## 240 10.2507 nan 0.0100 0.0347
## 260 9.4569 nan 0.0100 0.0268
## 280 8.8141 nan 0.0100 0.0222
## 300 8.2600 nan 0.0100 0.0260
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 59.5648 nan 0.0100 0.9044
## 2 58.6228 nan 0.0100 0.8239
## 3 57.7422 nan 0.0100 0.8790
## 4 56.8614 nan 0.0100 0.9225
## 5 55.9960 nan 0.0100 0.8283
## 6 55.1309 nan 0.0100 0.8546
## 7 54.1854 nan 0.0100 0.8393
## 8 53.3071 nan 0.0100 0.8552
## 9 52.4641 nan 0.0100 0.8578
## 10 51.6414 nan 0.0100 0.8407
## 20 44.1650 nan 0.0100 0.6280
## 40 32.7486 nan 0.0100 0.4340
## 60 24.6903 nan 0.0100 0.3268
## 80 19.0837 nan 0.0100 0.2384
## 100 15.1189 nan 0.0100 0.1512
## 120 12.2264 nan 0.0100 0.1064
## 140 10.1455 nan 0.0100 0.0819
## 160 8.5828 nan 0.0100 0.0560
## 180 7.3886 nan 0.0100 0.0495
## 200 6.5366 nan 0.0100 0.0363
## 220 5.8293 nan 0.0100 0.0200
## 240 5.3174 nan 0.0100 0.0156
## 260 4.9191 nan 0.0100 0.0101
## 280 4.6012 nan 0.0100 0.0056
## 300 4.3540 nan 0.0100 0.0035
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 59.5817 nan 0.0100 0.9281
## 2 58.5747 nan 0.0100 0.9947
## 3 57.6325 nan 0.0100 0.9581
## 4 56.7099 nan 0.0100 0.8773
## 5 55.8143 nan 0.0100 0.9412
## 6 54.9267 nan 0.0100 0.8364
## 7 54.0580 nan 0.0100 0.8937
## 8 53.2065 nan 0.0100 0.8718
## 9 52.3735 nan 0.0100 0.8637
## 10 51.5661 nan 0.0100 0.7839
## 20 44.0263 nan 0.0100 0.6910
## 40 32.5168 nan 0.0100 0.4663
## 60 24.6245 nan 0.0100 0.3795
## 80 19.0941 nan 0.0100 0.1941
## 100 15.1076 nan 0.0100 0.1754
## 120 12.2536 nan 0.0100 0.1118
## 140 10.1873 nan 0.0100 0.0859
## 160 8.6745 nan 0.0100 0.0573
## 180 7.4842 nan 0.0100 0.0458
## 200 6.6422 nan 0.0100 0.0350
## 220 5.9885 nan 0.0100 0.0114
## 240 5.4857 nan 0.0100 0.0135
## 260 5.0890 nan 0.0100 0.0083
## 280 4.7837 nan 0.0100 0.0040
## 300 4.5383 nan 0.0100 0.0013
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 59.5857 nan 0.0100 0.9209
## 2 58.5667 nan 0.0100 0.8821
## 3 57.6433 nan 0.0100 0.9076
## 4 56.7130 nan 0.0100 0.8524
## 5 55.7961 nan 0.0100 0.9505
## 6 54.9478 nan 0.0100 0.7982
## 7 54.0999 nan 0.0100 0.9649
## 8 53.2687 nan 0.0100 0.9195
## 9 52.4163 nan 0.0100 0.7728
## 10 51.6273 nan 0.0100 0.8040
## 20 44.0618 nan 0.0100 0.6643
## 40 32.7286 nan 0.0100 0.4396
## 60 25.0251 nan 0.0100 0.3287
## 80 19.4036 nan 0.0100 0.2325
## 100 15.4938 nan 0.0100 0.1422
## 120 12.6591 nan 0.0100 0.0958
## 140 10.6122 nan 0.0100 0.0734
## 160 9.0626 nan 0.0100 0.0541
## 180 7.9023 nan 0.0100 0.0488
## 200 7.0198 nan 0.0100 0.0331
## 220 6.3613 nan 0.0100 0.0293
## 240 5.8480 nan 0.0100 0.0178
## 260 5.4388 nan 0.0100 0.0058
## 280 5.1535 nan 0.0100 0.0098
## 300 4.9236 nan 0.0100 0.0025
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 59.5101 nan 0.0100 0.9904
## 2 58.4918 nan 0.0100 1.0195
## 3 57.5132 nan 0.0100 0.9932
## 4 56.4853 nan 0.0100 1.0311
## 5 55.5356 nan 0.0100 0.9417
## 6 54.5637 nan 0.0100 0.9455
## 7 53.6098 nan 0.0100 0.9397
## 8 52.7132 nan 0.0100 0.7587
## 9 51.7853 nan 0.0100 0.9676
## 10 50.9311 nan 0.0100 0.7840
## 20 43.1773 nan 0.0100 0.6776
## 40 31.3077 nan 0.0100 0.4155
## 60 23.0982 nan 0.0100 0.2787
## 80 17.2931 nan 0.0100 0.2317
## 100 13.3724 nan 0.0100 0.1454
## 120 10.5697 nan 0.0100 0.1123
## 140 8.5455 nan 0.0100 0.0773
## 160 7.0736 nan 0.0100 0.0459
## 180 6.0550 nan 0.0100 0.0277
## 200 5.2960 nan 0.0100 0.0195
## 220 4.7491 nan 0.0100 0.0189
## 240 4.3263 nan 0.0100 0.0039
## 260 4.0006 nan 0.0100 0.0060
## 280 3.7255 nan 0.0100 0.0021
## 300 3.5140 nan 0.0100 0.0003
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 59.4967 nan 0.0100 1.0480
## 2 58.5022 nan 0.0100 1.0844
## 3 57.4810 nan 0.0100 0.9313
## 4 56.5314 nan 0.0100 0.9639
## 5 55.4949 nan 0.0100 0.9813
## 6 54.5464 nan 0.0100 0.9588
## 7 53.7010 nan 0.0100 0.9853
## 8 52.7920 nan 0.0100 0.8156
## 9 51.8629 nan 0.0100 0.7145
## 10 50.9695 nan 0.0100 0.8524
## 20 43.2519 nan 0.0100 0.7130
## 40 31.1532 nan 0.0100 0.4942
## 60 23.0077 nan 0.0100 0.3057
## 80 17.3690 nan 0.0100 0.2364
## 100 13.4476 nan 0.0100 0.1563
## 120 10.7219 nan 0.0100 0.0936
## 140 8.7297 nan 0.0100 0.0689
## 160 7.3428 nan 0.0100 0.0412
## 180 6.3344 nan 0.0100 0.0357
## 200 5.5801 nan 0.0100 0.0224
## 220 5.0212 nan 0.0100 0.0163
## 240 4.6111 nan 0.0100 0.0061
## 260 4.3105 nan 0.0100 -0.0012
## 280 4.0639 nan 0.0100 0.0045
## 300 3.8846 nan 0.0100 -0.0016
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 59.5488 nan 0.0100 0.9925
## 2 58.5097 nan 0.0100 0.9841
## 3 57.5452 nan 0.0100 0.8939
## 4 56.5743 nan 0.0100 0.9740
## 5 55.6588 nan 0.0100 0.9509
## 6 54.7102 nan 0.0100 0.8030
## 7 53.7678 nan 0.0100 0.9736
## 8 52.8105 nan 0.0100 0.7952
## 9 51.9219 nan 0.0100 0.8898
## 10 51.0551 nan 0.0100 0.8353
## 20 43.1001 nan 0.0100 0.7881
## 40 31.1791 nan 0.0100 0.4949
## 60 23.1325 nan 0.0100 0.2781
## 80 17.6630 nan 0.0100 0.2352
## 100 13.7272 nan 0.0100 0.1388
## 120 11.0270 nan 0.0100 0.0967
## 140 9.1150 nan 0.0100 0.0683
## 160 7.7501 nan 0.0100 0.0476
## 180 6.7380 nan 0.0100 0.0327
## 200 6.0165 nan 0.0100 0.0292
## 220 5.4903 nan 0.0100 0.0121
## 240 5.1109 nan 0.0100 0.0114
## 260 4.8104 nan 0.0100 0.0025
## 280 4.5825 nan 0.0100 0.0073
## 300 4.4026 nan 0.0100 0.0021
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 56.7872 nan 0.0500 3.6766
## 2 53.2926 nan 0.0500 3.3236
## 3 50.0704 nan 0.0500 3.0223
## 4 47.6810 nan 0.0500 2.5563
## 5 45.3960 nan 0.0500 2.4589
## 6 42.9093 nan 0.0500 2.6793
## 7 40.5233 nan 0.0500 2.0671
## 8 38.5083 nan 0.0500 1.9015
## 9 36.6530 nan 0.0500 1.7910
## 10 34.8013 nan 0.0500 1.6317
## 20 22.6614 nan 0.0500 0.7718
## 40 12.0838 nan 0.0500 0.2686
## 60 7.9134 nan 0.0500 0.0918
## 80 6.0996 nan 0.0500 0.0308
## 100 5.1189 nan 0.0500 0.0385
## 120 4.6183 nan 0.0500 -0.0121
## 140 4.3756 nan 0.0500 -0.0003
## 160 4.2279 nan 0.0500 -0.0058
## 180 4.1508 nan 0.0500 -0.0153
## 200 4.0516 nan 0.0500 -0.0040
## 220 3.9592 nan 0.0500 -0.0304
## 240 3.9001 nan 0.0500 -0.0211
## 260 3.8370 nan 0.0500 -0.0336
## 280 3.7849 nan 0.0500 -0.0051
## 300 3.7344 nan 0.0500 -0.0206
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 56.6918 nan 0.0500 3.6445
## 2 53.5021 nan 0.0500 3.2461
## 3 50.2657 nan 0.0500 3.1762
## 4 47.6085 nan 0.0500 2.5090
## 5 44.8983 nan 0.0500 2.5774
## 6 42.6877 nan 0.0500 2.3453
## 7 40.5744 nan 0.0500 2.0587
## 8 38.4281 nan 0.0500 2.1082
## 9 36.3937 nan 0.0500 1.6577
## 10 34.5292 nan 0.0500 1.6177
## 20 22.5464 nan 0.0500 0.8994
## 40 12.1597 nan 0.0500 0.2889
## 60 8.1875 nan 0.0500 0.1153
## 80 6.2349 nan 0.0500 0.0581
## 100 5.3274 nan 0.0500 0.0296
## 120 4.8910 nan 0.0500 0.0066
## 140 4.6459 nan 0.0500 0.0034
## 160 4.4969 nan 0.0500 0.0005
## 180 4.3977 nan 0.0500 -0.0102
## 200 4.3036 nan 0.0500 0.0016
## 220 4.2196 nan 0.0500 -0.0091
## 240 4.1582 nan 0.0500 -0.0180
## 260 4.1127 nan 0.0500 -0.0065
## 280 4.0679 nan 0.0500 -0.0132
## 300 4.0204 nan 0.0500 -0.0115
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 56.8755 nan 0.0500 3.5794
## 2 53.7424 nan 0.0500 3.2878
## 3 50.6522 nan 0.0500 2.9833
## 4 47.9544 nan 0.0500 2.6395
## 5 45.4138 nan 0.0500 2.7439
## 6 42.8427 nan 0.0500 2.3994
## 7 40.7187 nan 0.0500 1.9552
## 8 38.7492 nan 0.0500 2.1480
## 9 36.9457 nan 0.0500 1.8365
## 10 35.1804 nan 0.0500 1.6570
## 20 22.7692 nan 0.0500 0.8627
## 40 12.1790 nan 0.0500 0.2706
## 60 8.2834 nan 0.0500 0.1099
## 80 6.4187 nan 0.0500 0.0150
## 100 5.5844 nan 0.0500 0.0312
## 120 5.1343 nan 0.0500 -0.0045
## 140 4.9207 nan 0.0500 0.0031
## 160 4.7661 nan 0.0500 -0.0061
## 180 4.6628 nan 0.0500 -0.0155
## 200 4.5723 nan 0.0500 0.0018
## 220 4.4969 nan 0.0500 -0.0043
## 240 4.4312 nan 0.0500 -0.0180
## 260 4.3907 nan 0.0500 -0.0033
## 280 4.3181 nan 0.0500 -0.0060
## 300 4.2693 nan 0.0500 -0.0094
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 55.5723 nan 0.0500 5.4751
## 2 51.2485 nan 0.0500 5.1950
## 3 47.3208 nan 0.0500 4.0114
## 4 43.6778 nan 0.0500 3.6748
## 5 40.5033 nan 0.0500 3.1818
## 6 37.4376 nan 0.0500 2.7304
## 7 34.6650 nan 0.0500 2.6490
## 8 32.0696 nan 0.0500 2.6748
## 9 29.8378 nan 0.0500 2.3310
## 10 27.8292 nan 0.0500 1.9549
## 20 14.7525 nan 0.0500 0.7964
## 40 6.3952 nan 0.0500 0.1315
## 60 4.3550 nan 0.0500 0.0326
## 80 3.6489 nan 0.0500 -0.0245
## 100 3.2780 nan 0.0500 -0.0102
## 120 3.0935 nan 0.0500 -0.0151
## 140 2.9114 nan 0.0500 -0.0059
## 160 2.7534 nan 0.0500 -0.0015
## 180 2.6077 nan 0.0500 -0.0138
## 200 2.4788 nan 0.0500 -0.0100
## 220 2.3569 nan 0.0500 -0.0035
## 240 2.2370 nan 0.0500 -0.0250
## 260 2.1518 nan 0.0500 -0.0152
## 280 2.0678 nan 0.0500 -0.0143
## 300 1.9833 nan 0.0500 -0.0192
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 55.4885 nan 0.0500 4.4900
## 2 51.3147 nan 0.0500 4.1321
## 3 47.6127 nan 0.0500 3.8366
## 4 44.0584 nan 0.0500 3.3136
## 5 40.7248 nan 0.0500 3.1548
## 6 37.8650 nan 0.0500 2.8126
## 7 34.9879 nan 0.0500 2.7318
## 8 32.5627 nan 0.0500 2.4684
## 9 30.2428 nan 0.0500 2.3586
## 10 28.2083 nan 0.0500 2.0613
## 20 14.9852 nan 0.0500 0.7585
## 40 6.5936 nan 0.0500 0.1921
## 60 4.5482 nan 0.0500 0.0183
## 80 3.9252 nan 0.0500 0.0021
## 100 3.6024 nan 0.0500 -0.0133
## 120 3.3923 nan 0.0500 -0.0066
## 140 3.2153 nan 0.0500 -0.0156
## 160 3.0899 nan 0.0500 -0.0239
## 180 2.9587 nan 0.0500 -0.0074
## 200 2.8516 nan 0.0500 -0.0246
## 220 2.7438 nan 0.0500 -0.0141
## 240 2.6329 nan 0.0500 -0.0048
## 260 2.5240 nan 0.0500 -0.0081
## 280 2.4396 nan 0.0500 -0.0062
## 300 2.3470 nan 0.0500 -0.0112
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 55.4685 nan 0.0500 4.4892
## 2 51.1385 nan 0.0500 3.8802
## 3 47.2735 nan 0.0500 3.9465
## 4 43.7178 nan 0.0500 3.0013
## 5 40.2901 nan 0.0500 3.1289
## 6 37.4141 nan 0.0500 2.8831
## 7 34.7332 nan 0.0500 2.7797
## 8 32.2570 nan 0.0500 2.3107
## 9 30.1355 nan 0.0500 2.1410
## 10 28.0738 nan 0.0500 1.8346
## 20 15.4129 nan 0.0500 0.6502
## 40 6.9285 nan 0.0500 0.1711
## 60 4.9471 nan 0.0500 0.0396
## 80 4.3511 nan 0.0500 -0.0267
## 100 4.0525 nan 0.0500 -0.0272
## 120 3.8253 nan 0.0500 -0.0603
## 140 3.6731 nan 0.0500 -0.0169
## 160 3.4932 nan 0.0500 -0.0080
## 180 3.3284 nan 0.0500 -0.0295
## 200 3.2131 nan 0.0500 -0.0124
## 220 3.1113 nan 0.0500 -0.0350
## 240 2.9976 nan 0.0500 -0.0142
## 260 2.8845 nan 0.0500 -0.0079
## 280 2.8048 nan 0.0500 -0.0185
## 300 2.7210 nan 0.0500 -0.0304
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 55.4433 nan 0.0500 4.6871
## 2 50.6566 nan 0.0500 4.4558
## 3 46.3076 nan 0.0500 4.8334
## 4 42.5614 nan 0.0500 3.4451
## 5 39.3690 nan 0.0500 3.2278
## 6 36.2337 nan 0.0500 2.7395
## 7 33.4289 nan 0.0500 2.6358
## 8 30.8133 nan 0.0500 2.5092
## 9 28.5011 nan 0.0500 2.1957
## 10 26.3029 nan 0.0500 2.0283
## 20 13.2484 nan 0.0500 0.7241
## 40 5.3378 nan 0.0500 0.0719
## 60 3.6090 nan 0.0500 0.0016
## 80 2.9822 nan 0.0500 0.0006
## 100 2.6148 nan 0.0500 -0.0128
## 120 2.3507 nan 0.0500 -0.0235
## 140 2.1540 nan 0.0500 -0.0133
## 160 1.9864 nan 0.0500 -0.0144
## 180 1.8405 nan 0.0500 -0.0140
## 200 1.6762 nan 0.0500 -0.0036
## 220 1.5499 nan 0.0500 -0.0067
## 240 1.4413 nan 0.0500 -0.0130
## 260 1.3428 nan 0.0500 -0.0096
## 280 1.2527 nan 0.0500 -0.0069
## 300 1.1683 nan 0.0500 -0.0093
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 55.5656 nan 0.0500 4.9245
## 2 50.9659 nan 0.0500 4.5981
## 3 46.7109 nan 0.0500 4.0706
## 4 43.0318 nan 0.0500 3.6478
## 5 39.5982 nan 0.0500 3.5874
## 6 36.2520 nan 0.0500 2.8755
## 7 33.6210 nan 0.0500 2.8678
## 8 31.0300 nan 0.0500 2.3172
## 9 28.6586 nan 0.0500 2.4129
## 10 26.4788 nan 0.0500 2.0020
## 20 13.3763 nan 0.0500 0.8368
## 40 5.4650 nan 0.0500 0.1065
## 60 3.8814 nan 0.0500 -0.0001
## 80 3.3449 nan 0.0500 -0.0239
## 100 2.9858 nan 0.0500 -0.0180
## 120 2.7205 nan 0.0500 -0.0177
## 140 2.5219 nan 0.0500 -0.0300
## 160 2.3445 nan 0.0500 -0.0133
## 180 2.2079 nan 0.0500 -0.0272
## 200 2.0839 nan 0.0500 -0.0065
## 220 1.9516 nan 0.0500 -0.0068
## 240 1.8472 nan 0.0500 -0.0146
## 260 1.7748 nan 0.0500 -0.0011
## 280 1.6891 nan 0.0500 -0.0134
## 300 1.6065 nan 0.0500 -0.0099
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 55.5143 nan 0.0500 4.9780
## 2 51.0793 nan 0.0500 4.4238
## 3 46.8782 nan 0.0500 3.8999
## 4 42.8993 nan 0.0500 3.9868
## 5 39.6739 nan 0.0500 3.1584
## 6 36.5759 nan 0.0500 2.9466
## 7 33.8833 nan 0.0500 2.6507
## 8 31.1700 nan 0.0500 2.2807
## 9 28.9239 nan 0.0500 2.4934
## 10 26.7299 nan 0.0500 2.2882
## 20 13.5371 nan 0.0500 0.8964
## 40 5.8025 nan 0.0500 0.0847
## 60 4.3024 nan 0.0500 0.0119
## 80 3.8625 nan 0.0500 -0.0205
## 100 3.5652 nan 0.0500 -0.0098
## 120 3.3974 nan 0.0500 -0.0242
## 140 3.1970 nan 0.0500 -0.0252
## 160 3.0094 nan 0.0500 -0.0187
## 180 2.8756 nan 0.0500 -0.0313
## 200 2.7444 nan 0.0500 -0.0184
## 220 2.6012 nan 0.0500 -0.0145
## 240 2.5021 nan 0.0500 -0.0072
## 260 2.4029 nan 0.0500 -0.0191
## 280 2.3015 nan 0.0500 -0.0163
## 300 2.1953 nan 0.0500 -0.0154
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 53.9176 nan 0.1000 7.0151
## 2 47.9532 nan 0.1000 5.5853
## 3 42.8926 nan 0.1000 4.8187
## 4 38.7388 nan 0.1000 3.8364
## 5 35.3977 nan 0.1000 3.6935
## 6 31.8647 nan 0.1000 3.4015
## 7 28.8960 nan 0.1000 2.6856
## 8 26.3654 nan 0.1000 2.1380
## 9 24.0134 nan 0.1000 1.9854
## 10 21.9709 nan 0.1000 2.0209
## 20 11.6744 nan 0.1000 0.4276
## 40 6.0735 nan 0.1000 0.1003
## 60 4.7100 nan 0.1000 0.0161
## 80 4.2892 nan 0.1000 -0.0251
## 100 4.1021 nan 0.1000 -0.0057
## 120 3.9508 nan 0.1000 -0.0309
## 140 3.8789 nan 0.1000 -0.0226
## 160 3.7843 nan 0.1000 -0.0029
## 180 3.6989 nan 0.1000 -0.0568
## 200 3.6470 nan 0.1000 -0.0236
## 220 3.5851 nan 0.1000 -0.0365
## 240 3.5099 nan 0.1000 -0.0345
## 260 3.4418 nan 0.1000 -0.0083
## 280 3.3784 nan 0.1000 -0.0396
## 300 3.3314 nan 0.1000 -0.0175
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 53.4171 nan 0.1000 6.6449
## 2 47.8300 nan 0.1000 5.6766
## 3 42.7724 nan 0.1000 3.8795
## 4 37.8536 nan 0.1000 4.4030
## 5 34.0978 nan 0.1000 3.5786
## 6 31.2104 nan 0.1000 2.0190
## 7 28.4670 nan 0.1000 2.4757
## 8 26.0355 nan 0.1000 1.9452
## 9 23.5444 nan 0.1000 2.2629
## 10 21.6720 nan 0.1000 1.8464
## 20 11.5154 nan 0.1000 0.5737
## 40 6.0920 nan 0.1000 0.0317
## 60 4.8708 nan 0.1000 -0.0191
## 80 4.5732 nan 0.1000 -0.0479
## 100 4.3684 nan 0.1000 -0.0290
## 120 4.2346 nan 0.1000 -0.0405
## 140 4.0871 nan 0.1000 -0.0198
## 160 3.9922 nan 0.1000 -0.0035
## 180 3.9139 nan 0.1000 -0.0402
## 200 3.8492 nan 0.1000 0.0051
## 220 3.7670 nan 0.1000 -0.0235
## 240 3.6923 nan 0.1000 -0.0083
## 260 3.6539 nan 0.1000 -0.0194
## 280 3.6077 nan 0.1000 -0.0291
## 300 3.5643 nan 0.1000 -0.0177
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 53.3800 nan 0.1000 7.0036
## 2 47.3159 nan 0.1000 6.1114
## 3 42.3875 nan 0.1000 5.2513
## 4 38.2181 nan 0.1000 3.8459
## 5 34.5876 nan 0.1000 3.6150
## 6 31.1349 nan 0.1000 3.0653
## 7 28.4608 nan 0.1000 2.6255
## 8 26.1302 nan 0.1000 2.3775
## 9 24.1076 nan 0.1000 1.9873
## 10 22.0113 nan 0.1000 2.1248
## 20 11.5955 nan 0.1000 0.4241
## 40 6.1258 nan 0.1000 0.0968
## 60 5.0537 nan 0.1000 -0.0656
## 80 4.6700 nan 0.1000 -0.0060
## 100 4.4820 nan 0.1000 -0.0005
## 120 4.3614 nan 0.1000 -0.0008
## 140 4.2283 nan 0.1000 -0.0178
## 160 4.1440 nan 0.1000 -0.0267
## 180 4.0592 nan 0.1000 -0.0315
## 200 3.9786 nan 0.1000 -0.0142
## 220 3.9305 nan 0.1000 -0.0163
## 240 3.8593 nan 0.1000 -0.0138
## 260 3.8084 nan 0.1000 -0.0116
## 280 3.7356 nan 0.1000 0.0007
## 300 3.6903 nan 0.1000 -0.0268
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 51.1298 nan 0.1000 9.2221
## 2 43.5610 nan 0.1000 8.6046
## 3 37.1065 nan 0.1000 6.0624
## 4 32.0674 nan 0.1000 5.0817
## 5 27.6947 nan 0.1000 4.5593
## 6 23.9011 nan 0.1000 3.4295
## 7 20.6683 nan 0.1000 3.1512
## 8 18.1921 nan 0.1000 1.8080
## 9 16.1280 nan 0.1000 2.1147
## 10 14.3391 nan 0.1000 1.4602
## 20 6.3692 nan 0.1000 0.2999
## 40 3.7310 nan 0.1000 0.0111
## 60 3.1778 nan 0.1000 -0.0836
## 80 2.7711 nan 0.1000 -0.0146
## 100 2.4656 nan 0.1000 -0.0200
## 120 2.2339 nan 0.1000 -0.0297
## 140 2.0230 nan 0.1000 -0.0246
## 160 1.8985 nan 0.1000 -0.0155
## 180 1.7470 nan 0.1000 -0.0117
## 200 1.6409 nan 0.1000 -0.0190
## 220 1.5303 nan 0.1000 -0.0177
## 240 1.4277 nan 0.1000 -0.0230
## 260 1.3392 nan 0.1000 -0.0174
## 280 1.2283 nan 0.1000 -0.0142
## 300 1.1600 nan 0.1000 -0.0167
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 50.9346 nan 0.1000 9.2742
## 2 43.2298 nan 0.1000 7.0547
## 3 36.4575 nan 0.1000 5.9730
## 4 31.0990 nan 0.1000 5.0957
## 5 27.0145 nan 0.1000 3.9160
## 6 23.5909 nan 0.1000 3.4839
## 7 20.4472 nan 0.1000 2.6777
## 8 17.8799 nan 0.1000 2.4049
## 9 15.7463 nan 0.1000 1.9826
## 10 14.2068 nan 0.1000 1.5676
## 20 6.2886 nan 0.1000 0.3123
## 40 3.9425 nan 0.1000 -0.0292
## 60 3.4702 nan 0.1000 -0.0300
## 80 3.1008 nan 0.1000 -0.0131
## 100 2.8624 nan 0.1000 -0.0200
## 120 2.6313 nan 0.1000 -0.0598
## 140 2.4602 nan 0.1000 -0.0303
## 160 2.3188 nan 0.1000 -0.0175
## 180 2.1772 nan 0.1000 -0.0405
## 200 2.0384 nan 0.1000 -0.0226
## 220 1.9101 nan 0.1000 -0.0192
## 240 1.7834 nan 0.1000 -0.0352
## 260 1.6996 nan 0.1000 -0.0160
## 280 1.6113 nan 0.1000 -0.0279
## 300 1.5246 nan 0.1000 -0.0093
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 51.5950 nan 0.1000 8.3794
## 2 43.8308 nan 0.1000 7.1214
## 3 37.8076 nan 0.1000 5.6016
## 4 32.8698 nan 0.1000 5.3205
## 5 27.8841 nan 0.1000 4.6785
## 6 24.5715 nan 0.1000 3.3179
## 7 21.5397 nan 0.1000 2.8568
## 8 18.8432 nan 0.1000 2.4496
## 9 16.8221 nan 0.1000 2.0181
## 10 15.2617 nan 0.1000 1.7046
## 20 6.6347 nan 0.1000 0.2833
## 40 4.2486 nan 0.1000 -0.0149
## 60 3.7787 nan 0.1000 -0.0190
## 80 3.4694 nan 0.1000 -0.0196
## 100 3.1947 nan 0.1000 -0.0373
## 120 3.0221 nan 0.1000 -0.0334
## 140 2.8385 nan 0.1000 -0.0276
## 160 2.6808 nan 0.1000 -0.0184
## 180 2.5116 nan 0.1000 -0.0285
## 200 2.4002 nan 0.1000 -0.0232
## 220 2.2783 nan 0.1000 -0.0380
## 240 2.1822 nan 0.1000 -0.0200
## 260 2.0864 nan 0.1000 -0.0368
## 280 1.9896 nan 0.1000 -0.0156
## 300 1.8993 nan 0.1000 -0.0295
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 50.6054 nan 0.1000 10.1424
## 2 42.5885 nan 0.1000 6.7176
## 3 35.6261 nan 0.1000 6.5610
## 4 30.0676 nan 0.1000 4.9483
## 5 25.4522 nan 0.1000 4.2897
## 6 21.8942 nan 0.1000 3.6321
## 7 18.8629 nan 0.1000 2.6835
## 8 16.4111 nan 0.1000 2.4666
## 9 14.4325 nan 0.1000 2.1045
## 10 12.4454 nan 0.1000 1.8635
## 20 5.2680 nan 0.1000 0.1775
## 40 3.0517 nan 0.1000 -0.0332
## 60 2.3445 nan 0.1000 -0.0274
## 80 1.9347 nan 0.1000 -0.0353
## 100 1.6053 nan 0.1000 -0.0128
## 120 1.3895 nan 0.1000 -0.0223
## 140 1.2440 nan 0.1000 -0.0083
## 160 1.0853 nan 0.1000 -0.0087
## 180 0.9643 nan 0.1000 -0.0029
## 200 0.8466 nan 0.1000 -0.0149
## 220 0.7593 nan 0.1000 -0.0125
## 240 0.6760 nan 0.1000 -0.0065
## 260 0.6214 nan 0.1000 -0.0128
## 280 0.5510 nan 0.1000 -0.0057
## 300 0.4948 nan 0.1000 -0.0075
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 49.9634 nan 0.1000 9.4014
## 2 41.8847 nan 0.1000 6.7158
## 3 35.2613 nan 0.1000 6.8687
## 4 29.7116 nan 0.1000 5.1108
## 5 25.2942 nan 0.1000 4.0963
## 6 21.4687 nan 0.1000 3.5762
## 7 18.4948 nan 0.1000 2.6667
## 8 15.9559 nan 0.1000 2.1789
## 9 13.9846 nan 0.1000 1.7370
## 10 12.4357 nan 0.1000 1.6308
## 20 5.1818 nan 0.1000 0.1900
## 40 3.2671 nan 0.1000 -0.0104
## 60 2.7578 nan 0.1000 -0.0256
## 80 2.4273 nan 0.1000 -0.0440
## 100 2.1215 nan 0.1000 -0.0201
## 120 1.8934 nan 0.1000 -0.0193
## 140 1.6978 nan 0.1000 -0.0406
## 160 1.5561 nan 0.1000 -0.0241
## 180 1.4241 nan 0.1000 -0.0314
## 200 1.2936 nan 0.1000 -0.0115
## 220 1.1826 nan 0.1000 -0.0127
## 240 1.0977 nan 0.1000 -0.0059
## 260 1.0046 nan 0.1000 -0.0160
## 280 0.9342 nan 0.1000 -0.0112
## 300 0.8744 nan 0.1000 -0.0290
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 50.6462 nan 0.1000 9.4627
## 2 42.5121 nan 0.1000 7.6239
## 3 35.8743 nan 0.1000 6.7384
## 4 30.4848 nan 0.1000 4.9400
## 5 26.1078 nan 0.1000 4.3720
## 6 22.3408 nan 0.1000 3.3748
## 7 19.2267 nan 0.1000 2.7493
## 8 16.7132 nan 0.1000 2.5066
## 9 14.7997 nan 0.1000 1.7164
## 10 13.1754 nan 0.1000 1.4000
## 20 5.9738 nan 0.1000 0.2272
## 40 3.9478 nan 0.1000 -0.0150
## 60 3.4497 nan 0.1000 -0.0536
## 80 3.1351 nan 0.1000 -0.0345
## 100 2.8915 nan 0.1000 -0.0718
## 120 2.6538 nan 0.1000 -0.0334
## 140 2.4287 nan 0.1000 -0.0142
## 160 2.2201 nan 0.1000 -0.0211
## 180 2.0823 nan 0.1000 -0.0655
## 200 1.9182 nan 0.1000 -0.0218
## 220 1.7647 nan 0.1000 -0.0211
## 240 1.6355 nan 0.1000 -0.0192
## 260 1.5266 nan 0.1000 -0.0150
## 280 1.4334 nan 0.1000 -0.0190
## 300 1.3407 nan 0.1000 -0.0257
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 62.3666 nan 0.0100 0.7745
## 2 61.6090 nan 0.0100 0.7517
## 3 60.8320 nan 0.0100 0.7918
## 4 60.0690 nan 0.0100 0.7150
## 5 59.3565 nan 0.0100 0.7037
## 6 58.6184 nan 0.0100 0.6815
## 7 57.8756 nan 0.0100 0.7089
## 8 57.2192 nan 0.0100 0.6890
## 9 56.5969 nan 0.0100 0.6257
## 10 55.9429 nan 0.0100 0.6358
## 20 49.7310 nan 0.0100 0.5790
## 40 40.1863 nan 0.0100 0.4189
## 60 32.9604 nan 0.0100 0.2811
## 80 27.5049 nan 0.0100 0.2147
## 100 23.3075 nan 0.0100 0.1674
## 120 20.0439 nan 0.0100 0.1249
## 140 17.4202 nan 0.0100 0.0916
## 160 15.3069 nan 0.0100 0.0914
## 180 13.5655 nan 0.0100 0.0534
## 200 12.1620 nan 0.0100 0.0569
## 220 10.9455 nan 0.0100 0.0415
## 240 9.9352 nan 0.0100 0.0391
## 260 9.0962 nan 0.0100 0.0338
## 280 8.3855 nan 0.0100 0.0300
## 300 7.7680 nan 0.0100 0.0221
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 62.3263 nan 0.0100 0.7230
## 2 61.6125 nan 0.0100 0.7691
## 3 60.8592 nan 0.0100 0.7790
## 4 60.0890 nan 0.0100 0.7706
## 5 59.3343 nan 0.0100 0.6986
## 6 58.6245 nan 0.0100 0.7201
## 7 57.8838 nan 0.0100 0.7145
## 8 57.1900 nan 0.0100 0.7245
## 9 56.4230 nan 0.0100 0.7275
## 10 55.7747 nan 0.0100 0.6913
## 20 49.6832 nan 0.0100 0.5411
## 40 40.0053 nan 0.0100 0.3880
## 60 32.9294 nan 0.0100 0.2949
## 80 27.5121 nan 0.0100 0.2086
## 100 23.2315 nan 0.0100 0.1756
## 120 19.8692 nan 0.0100 0.1261
## 140 17.2753 nan 0.0100 0.1054
## 160 15.1819 nan 0.0100 0.0764
## 180 13.4942 nan 0.0100 0.0621
## 200 12.1124 nan 0.0100 0.0519
## 220 10.9416 nan 0.0100 0.0515
## 240 9.9380 nan 0.0100 0.0171
## 260 9.0848 nan 0.0100 0.0393
## 280 8.3595 nan 0.0100 0.0307
## 300 7.7409 nan 0.0100 0.0142
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 62.4301 nan 0.0100 0.7655
## 2 61.6800 nan 0.0100 0.7735
## 3 60.8625 nan 0.0100 0.7237
## 4 60.0639 nan 0.0100 0.7596
## 5 59.3196 nan 0.0100 0.7467
## 6 58.6305 nan 0.0100 0.6892
## 7 57.8902 nan 0.0100 0.7108
## 8 57.2107 nan 0.0100 0.6974
## 9 56.4986 nan 0.0100 0.6530
## 10 55.8773 nan 0.0100 0.6445
## 20 49.8659 nan 0.0100 0.5837
## 40 40.3298 nan 0.0100 0.4291
## 60 33.0077 nan 0.0100 0.2939
## 80 27.5587 nan 0.0100 0.2352
## 100 23.2846 nan 0.0100 0.1626
## 120 19.9623 nan 0.0100 0.1516
## 140 17.3520 nan 0.0100 0.0961
## 160 15.2743 nan 0.0100 0.0711
## 180 13.6101 nan 0.0100 0.0628
## 200 12.1664 nan 0.0100 0.0507
## 220 11.0031 nan 0.0100 0.0404
## 240 10.0059 nan 0.0100 0.0360
## 260 9.1981 nan 0.0100 0.0285
## 280 8.5058 nan 0.0100 0.0207
## 300 7.9298 nan 0.0100 0.0208
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 62.1448 nan 0.0100 1.0570
## 2 61.1426 nan 0.0100 0.9295
## 3 60.1010 nan 0.0100 0.9063
## 4 59.1199 nan 0.0100 0.8452
## 5 58.1789 nan 0.0100 1.0006
## 6 57.2737 nan 0.0100 0.9247
## 7 56.3663 nan 0.0100 0.9373
## 8 55.4725 nan 0.0100 0.8479
## 9 54.5851 nan 0.0100 0.8785
## 10 53.7240 nan 0.0100 0.8542
## 20 45.8532 nan 0.0100 0.7787
## 40 33.7258 nan 0.0100 0.5471
## 60 25.3079 nan 0.0100 0.3943
## 80 19.3238 nan 0.0100 0.2046
## 100 15.1052 nan 0.0100 0.1924
## 120 12.0922 nan 0.0100 0.1081
## 140 9.8888 nan 0.0100 0.0924
## 160 8.3051 nan 0.0100 0.0500
## 180 7.1167 nan 0.0100 0.0381
## 200 6.2350 nan 0.0100 0.0351
## 220 5.5166 nan 0.0100 0.0268
## 240 4.9871 nan 0.0100 0.0219
## 260 4.5872 nan 0.0100 0.0122
## 280 4.2805 nan 0.0100 0.0053
## 300 4.0400 nan 0.0100 0.0096
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 62.0945 nan 0.0100 0.9786
## 2 61.1058 nan 0.0100 0.9705
## 3 60.0819 nan 0.0100 0.9370
## 4 59.1443 nan 0.0100 0.9501
## 5 58.1405 nan 0.0100 1.0091
## 6 57.2354 nan 0.0100 0.8829
## 7 56.3284 nan 0.0100 0.9700
## 8 55.4571 nan 0.0100 0.8453
## 9 54.5712 nan 0.0100 0.9180
## 10 53.7421 nan 0.0100 0.8607
## 20 45.8756 nan 0.0100 0.7534
## 40 33.6686 nan 0.0100 0.5340
## 60 25.2229 nan 0.0100 0.3554
## 80 19.3627 nan 0.0100 0.2164
## 100 15.1921 nan 0.0100 0.1725
## 120 12.2147 nan 0.0100 0.1181
## 140 9.9922 nan 0.0100 0.0832
## 160 8.4040 nan 0.0100 0.0616
## 180 7.2069 nan 0.0100 0.0563
## 200 6.3044 nan 0.0100 0.0246
## 220 5.6315 nan 0.0100 0.0232
## 240 5.0794 nan 0.0100 0.0218
## 260 4.6806 nan 0.0100 0.0092
## 280 4.3936 nan 0.0100 0.0122
## 300 4.1563 nan 0.0100 0.0073
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 62.1150 nan 0.0100 0.9482
## 2 61.0986 nan 0.0100 1.0356
## 3 60.0872 nan 0.0100 0.9700
## 4 59.0997 nan 0.0100 0.9243
## 5 58.0800 nan 0.0100 0.8601
## 6 57.1403 nan 0.0100 0.9063
## 7 56.2267 nan 0.0100 0.9634
## 8 55.2867 nan 0.0100 0.8690
## 9 54.3811 nan 0.0100 0.8761
## 10 53.5286 nan 0.0100 0.8338
## 20 45.7749 nan 0.0100 0.7665
## 40 33.5810 nan 0.0100 0.5015
## 60 25.3404 nan 0.0100 0.3192
## 80 19.4822 nan 0.0100 0.2446
## 100 15.3706 nan 0.0100 0.1522
## 120 12.3885 nan 0.0100 0.1043
## 140 10.1963 nan 0.0100 0.0853
## 160 8.5583 nan 0.0100 0.0634
## 180 7.3577 nan 0.0100 0.0370
## 200 6.4635 nan 0.0100 0.0328
## 220 5.8216 nan 0.0100 0.0234
## 240 5.2938 nan 0.0100 0.0191
## 260 4.9117 nan 0.0100 0.0092
## 280 4.6263 nan 0.0100 0.0061
## 300 4.4120 nan 0.0100 0.0036
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 62.0231 nan 0.0100 1.1812
## 2 60.9618 nan 0.0100 1.1027
## 3 59.9310 nan 0.0100 0.9604
## 4 58.8539 nan 0.0100 1.0465
## 5 57.7915 nan 0.0100 0.9646
## 6 56.7643 nan 0.0100 1.0248
## 7 55.7950 nan 0.0100 0.9430
## 8 54.7986 nan 0.0100 0.8944
## 9 53.8359 nan 0.0100 0.8376
## 10 52.8706 nan 0.0100 1.0157
## 20 44.5943 nan 0.0100 0.7622
## 40 31.9935 nan 0.0100 0.5424
## 60 23.3223 nan 0.0100 0.3732
## 80 17.3687 nan 0.0100 0.2161
## 100 13.3079 nan 0.0100 0.1714
## 120 10.4287 nan 0.0100 0.1139
## 140 8.3224 nan 0.0100 0.0582
## 160 6.8508 nan 0.0100 0.0498
## 180 5.7816 nan 0.0100 0.0445
## 200 5.0393 nan 0.0100 0.0229
## 220 4.4853 nan 0.0100 0.0100
## 240 4.0663 nan 0.0100 0.0108
## 260 3.7599 nan 0.0100 -0.0015
## 280 3.5120 nan 0.0100 0.0030
## 300 3.3146 nan 0.0100 0.0028
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 62.0327 nan 0.0100 1.0138
## 2 60.9327 nan 0.0100 1.0485
## 3 59.8853 nan 0.0100 1.0677
## 4 58.8338 nan 0.0100 0.8701
## 5 57.8401 nan 0.0100 1.0097
## 6 56.8605 nan 0.0100 0.8884
## 7 55.8420 nan 0.0100 0.9328
## 8 54.8976 nan 0.0100 0.9608
## 9 53.9937 nan 0.0100 0.9336
## 10 53.1256 nan 0.0100 0.9155
## 20 44.7059 nan 0.0100 0.6464
## 40 32.1114 nan 0.0100 0.4689
## 60 23.5668 nan 0.0100 0.3216
## 80 17.6377 nan 0.0100 0.2522
## 100 13.4722 nan 0.0100 0.1564
## 120 10.5870 nan 0.0100 0.1114
## 140 8.5470 nan 0.0100 0.0702
## 160 7.0802 nan 0.0100 0.0473
## 180 6.0294 nan 0.0100 0.0389
## 200 5.2730 nan 0.0100 0.0299
## 220 4.7071 nan 0.0100 0.0189
## 240 4.3004 nan 0.0100 0.0146
## 260 3.9658 nan 0.0100 0.0064
## 280 3.7325 nan 0.0100 0.0039
## 300 3.5570 nan 0.0100 0.0004
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 62.0248 nan 0.0100 1.1522
## 2 60.9473 nan 0.0100 1.1067
## 3 59.8982 nan 0.0100 1.1449
## 4 58.9010 nan 0.0100 1.0017
## 5 57.9225 nan 0.0100 0.9997
## 6 56.9768 nan 0.0100 0.9790
## 7 56.0655 nan 0.0100 0.9277
## 8 55.1037 nan 0.0100 0.9313
## 9 54.1773 nan 0.0100 0.9572
## 10 53.2365 nan 0.0100 0.8576
## 20 45.0227 nan 0.0100 0.6748
## 40 32.5234 nan 0.0100 0.4717
## 60 23.8651 nan 0.0100 0.3730
## 80 17.8533 nan 0.0100 0.1923
## 100 13.6444 nan 0.0100 0.1641
## 120 10.8320 nan 0.0100 0.0984
## 140 8.8153 nan 0.0100 0.0758
## 160 7.3935 nan 0.0100 0.0545
## 180 6.3434 nan 0.0100 0.0200
## 200 5.6228 nan 0.0100 0.0286
## 220 5.0891 nan 0.0100 0.0216
## 240 4.6669 nan 0.0100 0.0089
## 260 4.3635 nan 0.0100 0.0061
## 280 4.1354 nan 0.0100 0.0062
## 300 3.9642 nan 0.0100 0.0002
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 59.5047 nan 0.0500 3.7677
## 2 56.0395 nan 0.0500 3.5786
## 3 52.7698 nan 0.0500 3.1785
## 4 49.6318 nan 0.0500 2.8342
## 5 46.7270 nan 0.0500 2.5422
## 6 44.2266 nan 0.0500 1.9435
## 7 41.7435 nan 0.0500 1.9892
## 8 39.8471 nan 0.0500 1.7402
## 9 37.7459 nan 0.0500 1.9216
## 10 36.0801 nan 0.0500 1.7574
## 20 23.1064 nan 0.0500 0.9461
## 40 11.8094 nan 0.0500 0.2641
## 60 7.5838 nan 0.0500 0.0784
## 80 5.7318 nan 0.0500 0.0635
## 100 4.8478 nan 0.0500 0.0088
## 120 4.3847 nan 0.0500 0.0160
## 140 4.1562 nan 0.0500 -0.0013
## 160 4.0257 nan 0.0500 0.0037
## 180 3.9240 nan 0.0500 -0.0007
## 200 3.8486 nan 0.0500 -0.0040
## 220 3.7745 nan 0.0500 -0.0050
## 240 3.7282 nan 0.0500 -0.0155
## 260 3.6757 nan 0.0500 -0.0077
## 280 3.6302 nan 0.0500 -0.0013
## 300 3.5931 nan 0.0500 -0.0148
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 59.2775 nan 0.0500 3.8648
## 2 55.4966 nan 0.0500 3.3671
## 3 52.3059 nan 0.0500 3.1785
## 4 49.3441 nan 0.0500 2.9452
## 5 46.4254 nan 0.0500 2.5125
## 6 43.9975 nan 0.0500 2.1511
## 7 41.8546 nan 0.0500 2.2623
## 8 39.4682 nan 0.0500 2.0702
## 9 37.4819 nan 0.0500 1.9601
## 10 35.6142 nan 0.0500 1.7867
## 20 23.1449 nan 0.0500 0.9512
## 40 12.1609 nan 0.0500 0.2582
## 60 7.7496 nan 0.0500 0.0660
## 80 5.8031 nan 0.0500 0.0318
## 100 4.8564 nan 0.0500 0.0172
## 120 4.3577 nan 0.0500 0.0121
## 140 4.1329 nan 0.0500 -0.0125
## 160 4.0210 nan 0.0500 -0.0167
## 180 3.8969 nan 0.0500 -0.0128
## 200 3.8363 nan 0.0500 -0.0048
## 220 3.7871 nan 0.0500 -0.0160
## 240 3.7364 nan 0.0500 -0.0233
## 260 3.6812 nan 0.0500 -0.0068
## 280 3.6470 nan 0.0500 -0.0031
## 300 3.6168 nan 0.0500 -0.0170
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 59.4076 nan 0.0500 3.6972
## 2 55.6875 nan 0.0500 3.6430
## 3 52.4039 nan 0.0500 3.2392
## 4 49.5561 nan 0.0500 2.7492
## 5 46.6213 nan 0.0500 2.6466
## 6 43.9525 nan 0.0500 2.3052
## 7 41.5011 nan 0.0500 2.4400
## 8 39.5083 nan 0.0500 1.7253
## 9 37.6321 nan 0.0500 1.8658
## 10 35.8106 nan 0.0500 1.8050
## 20 22.9937 nan 0.0500 0.8894
## 40 11.9232 nan 0.0500 0.3240
## 60 7.6978 nan 0.0500 0.0949
## 80 5.8189 nan 0.0500 0.0542
## 100 4.9626 nan 0.0500 0.0175
## 120 4.5492 nan 0.0500 -0.0110
## 140 4.3535 nan 0.0500 -0.0063
## 160 4.2300 nan 0.0500 -0.0040
## 180 4.1492 nan 0.0500 -0.0149
## 200 4.0676 nan 0.0500 -0.0089
## 220 4.0177 nan 0.0500 -0.0094
## 240 3.9537 nan 0.0500 -0.0101
## 260 3.9014 nan 0.0500 -0.0129
## 280 3.8561 nan 0.0500 -0.0169
## 300 3.7889 nan 0.0500 -0.0061
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 57.9732 nan 0.0500 4.8411
## 2 53.3895 nan 0.0500 4.8857
## 3 49.0053 nan 0.0500 4.0397
## 4 45.2499 nan 0.0500 3.7254
## 5 41.6551 nan 0.0500 3.2348
## 6 38.5672 nan 0.0500 3.4127
## 7 35.8895 nan 0.0500 2.4979
## 8 33.6354 nan 0.0500 2.2063
## 9 31.2132 nan 0.0500 2.3220
## 10 29.0091 nan 0.0500 2.2349
## 20 15.1794 nan 0.0500 0.7898
## 40 6.2874 nan 0.0500 0.2063
## 60 4.0876 nan 0.0500 0.0270
## 80 3.4852 nan 0.0500 -0.0080
## 100 3.1574 nan 0.0500 -0.0400
## 120 2.9230 nan 0.0500 -0.0141
## 140 2.7211 nan 0.0500 -0.0176
## 160 2.5266 nan 0.0500 -0.0112
## 180 2.3917 nan 0.0500 -0.0220
## 200 2.2729 nan 0.0500 -0.0162
## 220 2.1668 nan 0.0500 -0.0175
## 240 2.0715 nan 0.0500 -0.0124
## 260 1.9873 nan 0.0500 -0.0093
## 280 1.9056 nan 0.0500 -0.0104
## 300 1.8280 nan 0.0500 -0.0146
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 58.0690 nan 0.0500 4.7817
## 2 53.4089 nan 0.0500 4.6694
## 3 49.2056 nan 0.0500 4.1396
## 4 45.3607 nan 0.0500 3.6699
## 5 41.7697 nan 0.0500 3.5068
## 6 38.4963 nan 0.0500 2.5788
## 7 35.5744 nan 0.0500 3.0243
## 8 32.8960 nan 0.0500 2.5875
## 9 30.5334 nan 0.0500 2.3667
## 10 28.3511 nan 0.0500 1.9365
## 20 14.7342 nan 0.0500 0.7733
## 40 6.0869 nan 0.0500 0.1687
## 60 4.0585 nan 0.0500 0.0310
## 80 3.5689 nan 0.0500 -0.0117
## 100 3.2529 nan 0.0500 -0.0070
## 120 3.0690 nan 0.0500 -0.0036
## 140 2.9074 nan 0.0500 -0.0097
## 160 2.7547 nan 0.0500 -0.0212
## 180 2.6300 nan 0.0500 -0.0102
## 200 2.5325 nan 0.0500 -0.0127
## 220 2.4488 nan 0.0500 -0.0101
## 240 2.3538 nan 0.0500 -0.0079
## 260 2.2704 nan 0.0500 -0.0106
## 280 2.1898 nan 0.0500 -0.0093
## 300 2.1107 nan 0.0500 -0.0033
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 58.3457 nan 0.0500 4.5497
## 2 53.7352 nan 0.0500 4.4274
## 3 49.4504 nan 0.0500 4.3940
## 4 45.7361 nan 0.0500 3.8289
## 5 42.3187 nan 0.0500 3.3659
## 6 39.2154 nan 0.0500 2.9067
## 7 36.4085 nan 0.0500 2.8827
## 8 33.6382 nan 0.0500 2.6308
## 9 31.2280 nan 0.0500 2.3426
## 10 29.0276 nan 0.0500 1.8932
## 20 15.2903 nan 0.0500 0.6671
## 40 6.4833 nan 0.0500 0.1260
## 60 4.4829 nan 0.0500 -0.0042
## 80 3.9029 nan 0.0500 -0.0028
## 100 3.6449 nan 0.0500 -0.0123
## 120 3.4567 nan 0.0500 -0.0344
## 140 3.2980 nan 0.0500 -0.0188
## 160 3.1484 nan 0.0500 -0.0196
## 180 3.0318 nan 0.0500 -0.0157
## 200 2.9109 nan 0.0500 -0.0102
## 220 2.8270 nan 0.0500 -0.0177
## 240 2.7271 nan 0.0500 -0.0157
## 260 2.6546 nan 0.0500 -0.0067
## 280 2.5600 nan 0.0500 -0.0280
## 300 2.4856 nan 0.0500 -0.0151
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 57.6555 nan 0.0500 5.4898
## 2 52.7172 nan 0.0500 4.8280
## 3 48.3536 nan 0.0500 4.1629
## 4 44.3129 nan 0.0500 3.7626
## 5 40.7107 nan 0.0500 3.4519
## 6 37.3951 nan 0.0500 3.6380
## 7 34.2625 nan 0.0500 2.9334
## 8 31.7380 nan 0.0500 2.5877
## 9 29.3210 nan 0.0500 2.3554
## 10 27.1057 nan 0.0500 2.0537
## 20 12.8948 nan 0.0500 0.8838
## 40 4.9318 nan 0.0500 0.1199
## 60 3.3565 nan 0.0500 0.0068
## 80 2.7717 nan 0.0500 -0.0035
## 100 2.4331 nan 0.0500 -0.0127
## 120 2.1977 nan 0.0500 -0.0158
## 140 1.9806 nan 0.0500 -0.0136
## 160 1.8217 nan 0.0500 -0.0191
## 180 1.6777 nan 0.0500 -0.0134
## 200 1.5573 nan 0.0500 -0.0139
## 220 1.4476 nan 0.0500 -0.0154
## 240 1.3314 nan 0.0500 -0.0062
## 260 1.2583 nan 0.0500 -0.0125
## 280 1.1744 nan 0.0500 -0.0130
## 300 1.0928 nan 0.0500 -0.0097
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 57.7410 nan 0.0500 5.4032
## 2 52.8961 nan 0.0500 4.9490
## 3 48.3202 nan 0.0500 3.9978
## 4 44.2521 nan 0.0500 4.1680
## 5 40.6345 nan 0.0500 3.5487
## 6 37.3365 nan 0.0500 3.1174
## 7 34.3868 nan 0.0500 3.0625
## 8 31.6272 nan 0.0500 2.9383
## 9 29.3122 nan 0.0500 2.4324
## 10 27.0507 nan 0.0500 2.2717
## 20 13.0452 nan 0.0500 0.8046
## 40 5.1410 nan 0.0500 0.1283
## 60 3.6367 nan 0.0500 -0.0036
## 80 3.1242 nan 0.0500 0.0044
## 100 2.8473 nan 0.0500 -0.0204
## 120 2.6180 nan 0.0500 -0.0218
## 140 2.4372 nan 0.0500 -0.0248
## 160 2.2681 nan 0.0500 -0.0075
## 180 2.1267 nan 0.0500 -0.0161
## 200 1.9981 nan 0.0500 -0.0139
## 220 1.8901 nan 0.0500 -0.0152
## 240 1.7892 nan 0.0500 -0.0174
## 260 1.6901 nan 0.0500 -0.0125
## 280 1.6133 nan 0.0500 -0.0183
## 300 1.5347 nan 0.0500 -0.0112
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 57.7746 nan 0.0500 5.3603
## 2 52.8792 nan 0.0500 4.6078
## 3 48.6105 nan 0.0500 4.2651
## 4 44.5674 nan 0.0500 4.0778
## 5 40.8576 nan 0.0500 3.8866
## 6 37.6271 nan 0.0500 3.2488
## 7 34.4510 nan 0.0500 2.6504
## 8 31.8512 nan 0.0500 2.5021
## 9 29.3322 nan 0.0500 2.2103
## 10 27.3026 nan 0.0500 2.0951
## 20 13.3943 nan 0.0500 0.7328
## 40 5.3889 nan 0.0500 0.0966
## 60 3.9447 nan 0.0500 0.0174
## 80 3.4681 nan 0.0500 -0.0155
## 100 3.1949 nan 0.0500 -0.0243
## 120 2.9657 nan 0.0500 -0.0172
## 140 2.7987 nan 0.0500 -0.0123
## 160 2.6507 nan 0.0500 -0.0213
## 180 2.5054 nan 0.0500 -0.0159
## 200 2.4032 nan 0.0500 -0.0183
## 220 2.2974 nan 0.0500 -0.0198
## 240 2.2013 nan 0.0500 -0.0145
## 260 2.1075 nan 0.0500 -0.0102
## 280 2.0293 nan 0.0500 -0.0103
## 300 1.9488 nan 0.0500 -0.0078
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 56.0803 nan 0.1000 7.5313
## 2 49.8452 nan 0.1000 6.4820
## 3 44.4064 nan 0.1000 5.3005
## 4 39.5259 nan 0.1000 4.3813
## 5 35.7528 nan 0.1000 3.4928
## 6 32.4886 nan 0.1000 3.1382
## 7 29.2798 nan 0.1000 2.6986
## 8 27.0270 nan 0.1000 2.3115
## 9 24.7436 nan 0.1000 1.6704
## 10 22.8004 nan 0.1000 1.7838
## 20 11.8109 nan 0.1000 0.3964
## 40 5.7387 nan 0.1000 0.0661
## 60 4.4420 nan 0.1000 0.0035
## 80 4.0839 nan 0.1000 -0.0017
## 100 3.9172 nan 0.1000 -0.0297
## 120 3.8046 nan 0.1000 -0.0303
## 140 3.7075 nan 0.1000 -0.0330
## 160 3.6285 nan 0.1000 -0.0092
## 180 3.5439 nan 0.1000 -0.0183
## 200 3.4701 nan 0.1000 -0.0327
## 220 3.3994 nan 0.1000 -0.0170
## 240 3.3283 nan 0.1000 -0.0206
## 260 3.2703 nan 0.1000 -0.0390
## 280 3.2279 nan 0.1000 -0.0189
## 300 3.1826 nan 0.1000 -0.0113
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 55.2672 nan 0.1000 7.1844
## 2 48.6712 nan 0.1000 6.1719
## 3 43.1145 nan 0.1000 4.9617
## 4 38.7978 nan 0.1000 4.1956
## 5 34.6457 nan 0.1000 3.5746
## 6 31.7696 nan 0.1000 2.8539
## 7 28.6772 nan 0.1000 2.7944
## 8 26.1788 nan 0.1000 2.3845
## 9 24.4291 nan 0.1000 1.6301
## 10 22.5347 nan 0.1000 1.6783
## 20 11.6077 nan 0.1000 0.6700
## 40 5.6273 nan 0.1000 0.0853
## 60 4.3418 nan 0.1000 0.0061
## 80 4.0344 nan 0.1000 0.0034
## 100 3.8896 nan 0.1000 -0.0084
## 120 3.8039 nan 0.1000 -0.0228
## 140 3.7190 nan 0.1000 -0.0123
## 160 3.6533 nan 0.1000 -0.0226
## 180 3.5778 nan 0.1000 -0.0046
## 200 3.4911 nan 0.1000 -0.0160
## 220 3.4363 nan 0.1000 -0.0322
## 240 3.3747 nan 0.1000 -0.0105
## 260 3.3236 nan 0.1000 -0.0145
## 280 3.2648 nan 0.1000 -0.0037
## 300 3.2304 nan 0.1000 -0.0094
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 55.6990 nan 0.1000 7.2768
## 2 49.6177 nan 0.1000 6.0675
## 3 44.4808 nan 0.1000 4.9048
## 4 39.9892 nan 0.1000 4.3990
## 5 35.7152 nan 0.1000 3.9322
## 6 31.9686 nan 0.1000 2.8026
## 7 28.5617 nan 0.1000 2.7880
## 8 26.1725 nan 0.1000 2.2050
## 9 24.1058 nan 0.1000 2.1288
## 10 22.3283 nan 0.1000 1.6598
## 20 11.9049 nan 0.1000 0.6622
## 40 5.7876 nan 0.1000 0.0838
## 60 4.5613 nan 0.1000 0.0108
## 80 4.2351 nan 0.1000 -0.0362
## 100 4.0641 nan 0.1000 -0.0189
## 120 3.9528 nan 0.1000 -0.0228
## 140 3.8823 nan 0.1000 -0.0520
## 160 3.7857 nan 0.1000 -0.0046
## 180 3.7246 nan 0.1000 -0.0288
## 200 3.6581 nan 0.1000 -0.0231
## 220 3.5823 nan 0.1000 -0.0199
## 240 3.5387 nan 0.1000 -0.0081
## 260 3.4868 nan 0.1000 -0.0212
## 280 3.4314 nan 0.1000 -0.0171
## 300 3.3769 nan 0.1000 -0.0173
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 53.7290 nan 0.1000 9.9066
## 2 45.6315 nan 0.1000 8.6165
## 3 38.9216 nan 0.1000 5.6129
## 4 33.4303 nan 0.1000 4.8112
## 5 28.5054 nan 0.1000 4.7638
## 6 24.6004 nan 0.1000 3.7476
## 7 21.3019 nan 0.1000 3.2325
## 8 18.4851 nan 0.1000 2.4128
## 9 16.3974 nan 0.1000 1.7965
## 10 14.6864 nan 0.1000 1.7041
## 20 6.0140 nan 0.1000 0.2691
## 40 3.4859 nan 0.1000 -0.0160
## 60 2.8725 nan 0.1000 -0.0297
## 80 2.5612 nan 0.1000 -0.0280
## 100 2.3114 nan 0.1000 -0.0246
## 120 2.1012 nan 0.1000 -0.0331
## 140 1.9231 nan 0.1000 -0.0209
## 160 1.7715 nan 0.1000 -0.0442
## 180 1.6248 nan 0.1000 -0.0098
## 200 1.4951 nan 0.1000 -0.0213
## 220 1.4162 nan 0.1000 -0.0162
## 240 1.3494 nan 0.1000 -0.0130
## 260 1.2600 nan 0.1000 -0.0070
## 280 1.1910 nan 0.1000 -0.0118
## 300 1.1261 nan 0.1000 -0.0153
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 53.2377 nan 0.1000 9.4861
## 2 45.1783 nan 0.1000 7.9912
## 3 38.9060 nan 0.1000 6.6918
## 4 33.1962 nan 0.1000 5.1613
## 5 28.5414 nan 0.1000 4.1617
## 6 24.7137 nan 0.1000 3.8046
## 7 21.3667 nan 0.1000 3.3157
## 8 18.6283 nan 0.1000 2.3949
## 9 16.3098 nan 0.1000 2.0543
## 10 14.4778 nan 0.1000 1.6397
## 20 6.0105 nan 0.1000 0.3172
## 40 3.5240 nan 0.1000 -0.0144
## 60 3.0721 nan 0.1000 -0.0438
## 80 2.7545 nan 0.1000 -0.0212
## 100 2.5652 nan 0.1000 -0.0618
## 120 2.3642 nan 0.1000 -0.0091
## 140 2.2069 nan 0.1000 -0.0150
## 160 2.0859 nan 0.1000 -0.0312
## 180 1.9946 nan 0.1000 -0.0207
## 200 1.8617 nan 0.1000 -0.0149
## 220 1.7648 nan 0.1000 -0.0204
## 240 1.7029 nan 0.1000 -0.0175
## 260 1.6133 nan 0.1000 -0.0136
## 280 1.5417 nan 0.1000 -0.0215
## 300 1.4541 nan 0.1000 -0.0179
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 53.2146 nan 0.1000 9.3483
## 2 45.1602 nan 0.1000 8.6411
## 3 38.5428 nan 0.1000 7.0999
## 4 32.8184 nan 0.1000 5.8876
## 5 27.8858 nan 0.1000 4.2108
## 6 24.4795 nan 0.1000 3.6876
## 7 21.7731 nan 0.1000 2.9373
## 8 19.2584 nan 0.1000 2.5896
## 9 17.0369 nan 0.1000 2.2264
## 10 15.2640 nan 0.1000 1.6736
## 20 6.8441 nan 0.1000 0.4335
## 40 3.9777 nan 0.1000 -0.0053
## 60 3.4322 nan 0.1000 -0.0283
## 80 3.1130 nan 0.1000 -0.0325
## 100 2.9361 nan 0.1000 -0.0429
## 120 2.7311 nan 0.1000 -0.0213
## 140 2.5908 nan 0.1000 -0.0211
## 160 2.4328 nan 0.1000 -0.0197
## 180 2.3119 nan 0.1000 -0.0261
## 200 2.1803 nan 0.1000 -0.0191
## 220 2.0844 nan 0.1000 -0.0252
## 240 1.9988 nan 0.1000 -0.0223
## 260 1.9351 nan 0.1000 -0.0349
## 280 1.8526 nan 0.1000 -0.0164
## 300 1.7735 nan 0.1000 -0.0290
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 52.6829 nan 0.1000 9.4575
## 2 43.9227 nan 0.1000 8.7425
## 3 37.3367 nan 0.1000 6.6163
## 4 31.4983 nan 0.1000 6.0197
## 5 26.6240 nan 0.1000 4.4412
## 6 22.5364 nan 0.1000 3.9279
## 7 19.4797 nan 0.1000 2.8998
## 8 16.7500 nan 0.1000 2.4887
## 9 14.5386 nan 0.1000 2.1272
## 10 12.8489 nan 0.1000 1.5969
## 20 4.8985 nan 0.1000 0.2027
## 40 2.6990 nan 0.1000 0.0051
## 60 2.0987 nan 0.1000 -0.0021
## 80 1.7814 nan 0.1000 -0.0334
## 100 1.5208 nan 0.1000 -0.0424
## 120 1.3232 nan 0.1000 -0.0152
## 140 1.1465 nan 0.1000 -0.0172
## 160 1.0038 nan 0.1000 -0.0061
## 180 0.8904 nan 0.1000 -0.0120
## 200 0.8049 nan 0.1000 -0.0174
## 220 0.7198 nan 0.1000 -0.0115
## 240 0.6522 nan 0.1000 -0.0102
## 260 0.5868 nan 0.1000 -0.0060
## 280 0.5340 nan 0.1000 -0.0056
## 300 0.4855 nan 0.1000 -0.0068
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 52.4775 nan 0.1000 10.7340
## 2 43.9951 nan 0.1000 9.1355
## 3 37.0396 nan 0.1000 6.6694
## 4 31.3262 nan 0.1000 5.4702
## 5 26.7468 nan 0.1000 4.4205
## 6 22.8081 nan 0.1000 3.5088
## 7 19.6570 nan 0.1000 3.2550
## 8 17.0246 nan 0.1000 2.6802
## 9 14.9335 nan 0.1000 2.1329
## 10 13.0306 nan 0.1000 1.7657
## 20 5.0612 nan 0.1000 0.2234
## 40 3.0807 nan 0.1000 -0.0271
## 60 2.5598 nan 0.1000 -0.0504
## 80 2.2123 nan 0.1000 -0.0337
## 100 1.9912 nan 0.1000 -0.0299
## 120 1.7813 nan 0.1000 -0.0190
## 140 1.5883 nan 0.1000 -0.0245
## 160 1.4691 nan 0.1000 -0.0220
## 180 1.3515 nan 0.1000 -0.0289
## 200 1.2447 nan 0.1000 -0.0226
## 220 1.1206 nan 0.1000 -0.0152
## 240 1.0433 nan 0.1000 -0.0175
## 260 0.9514 nan 0.1000 -0.0061
## 280 0.8781 nan 0.1000 -0.0134
## 300 0.8163 nan 0.1000 -0.0180
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 52.5734 nan 0.1000 10.2438
## 2 43.7559 nan 0.1000 8.0896
## 3 36.3089 nan 0.1000 7.4155
## 4 30.8100 nan 0.1000 5.3545
## 5 26.1630 nan 0.1000 4.8181
## 6 22.3324 nan 0.1000 4.0317
## 7 19.0919 nan 0.1000 2.8747
## 8 16.7111 nan 0.1000 2.3354
## 9 14.4597 nan 0.1000 1.8226
## 10 12.7555 nan 0.1000 1.9091
## 20 5.3070 nan 0.1000 0.2227
## 40 3.4909 nan 0.1000 -0.0119
## 60 3.0583 nan 0.1000 -0.0497
## 80 2.7795 nan 0.1000 -0.0440
## 100 2.5366 nan 0.1000 -0.0360
## 120 2.2867 nan 0.1000 -0.0200
## 140 2.1206 nan 0.1000 -0.0243
## 160 1.9255 nan 0.1000 -0.0256
## 180 1.7683 nan 0.1000 -0.0373
## 200 1.6424 nan 0.1000 -0.0112
## 220 1.5166 nan 0.1000 -0.0214
## 240 1.4146 nan 0.1000 -0.0242
## 260 1.3280 nan 0.1000 -0.0139
## 280 1.2480 nan 0.1000 -0.0141
## 300 1.1826 nan 0.1000 -0.0210
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 59.2055 nan 0.0100 0.7649
## 2 58.4764 nan 0.0100 0.6512
## 3 57.7538 nan 0.0100 0.8148
## 4 57.0655 nan 0.0100 0.6700
## 5 56.4288 nan 0.0100 0.6855
## 6 55.7148 nan 0.0100 0.6332
## 7 55.0100 nan 0.0100 0.7080
## 8 54.3008 nan 0.0100 0.7489
## 9 53.6347 nan 0.0100 0.6366
## 10 53.0220 nan 0.0100 0.6009
## 20 47.3155 nan 0.0100 0.5153
## 40 38.1744 nan 0.0100 0.3640
## 60 31.3245 nan 0.0100 0.2694
## 80 26.0187 nan 0.0100 0.2143
## 100 21.9611 nan 0.0100 0.1632
## 120 18.7910 nan 0.0100 0.1068
## 140 16.3227 nan 0.0100 0.0925
## 160 14.3408 nan 0.0100 0.0745
## 180 12.7449 nan 0.0100 0.0695
## 200 11.4512 nan 0.0100 0.0451
## 220 10.4004 nan 0.0100 0.0416
## 240 9.4956 nan 0.0100 0.0405
## 260 8.7639 nan 0.0100 0.0177
## 280 8.1107 nan 0.0100 0.0228
## 300 7.5627 nan 0.0100 0.0236
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 59.2172 nan 0.0100 0.7959
## 2 58.5000 nan 0.0100 0.7490
## 3 57.7432 nan 0.0100 0.6952
## 4 57.0722 nan 0.0100 0.7074
## 5 56.4215 nan 0.0100 0.5754
## 6 55.7722 nan 0.0100 0.6623
## 7 55.1252 nan 0.0100 0.6396
## 8 54.4376 nan 0.0100 0.6706
## 9 53.8144 nan 0.0100 0.6113
## 10 53.1204 nan 0.0100 0.6285
## 20 47.4312 nan 0.0100 0.5403
## 40 38.1606 nan 0.0100 0.3735
## 60 31.3166 nan 0.0100 0.3128
## 80 26.0916 nan 0.0100 0.2193
## 100 21.9502 nan 0.0100 0.1467
## 120 18.8509 nan 0.0100 0.1279
## 140 16.3704 nan 0.0100 0.0999
## 160 14.3454 nan 0.0100 0.0862
## 180 12.7406 nan 0.0100 0.0481
## 200 11.4441 nan 0.0100 0.0528
## 220 10.3588 nan 0.0100 0.0419
## 240 9.4300 nan 0.0100 0.0376
## 260 8.6832 nan 0.0100 0.0310
## 280 8.0290 nan 0.0100 0.0174
## 300 7.5103 nan 0.0100 0.0190
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 59.2296 nan 0.0100 0.7425
## 2 58.5102 nan 0.0100 0.7955
## 3 57.8199 nan 0.0100 0.6897
## 4 57.1155 nan 0.0100 0.7518
## 5 56.4129 nan 0.0100 0.6998
## 6 55.6816 nan 0.0100 0.6536
## 7 54.9794 nan 0.0100 0.6278
## 8 54.3355 nan 0.0100 0.6689
## 9 53.6976 nan 0.0100 0.6181
## 10 53.0504 nan 0.0100 0.4874
## 20 47.2064 nan 0.0100 0.5202
## 40 38.1963 nan 0.0100 0.3396
## 60 31.4038 nan 0.0100 0.2507
## 80 26.1567 nan 0.0100 0.2194
## 100 22.0882 nan 0.0100 0.1706
## 120 18.8968 nan 0.0100 0.1227
## 140 16.3688 nan 0.0100 0.0879
## 160 14.4039 nan 0.0100 0.0648
## 180 12.7744 nan 0.0100 0.0663
## 200 11.4732 nan 0.0100 0.0379
## 220 10.3995 nan 0.0100 0.0443
## 240 9.5005 nan 0.0100 0.0298
## 260 8.7740 nan 0.0100 0.0295
## 280 8.1373 nan 0.0100 0.0192
## 300 7.5859 nan 0.0100 0.0206
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 58.9881 nan 0.0100 0.9252
## 2 57.9850 nan 0.0100 0.8941
## 3 57.0249 nan 0.0100 0.9680
## 4 56.0919 nan 0.0100 1.0039
## 5 55.1750 nan 0.0100 1.0136
## 6 54.2720 nan 0.0100 0.9128
## 7 53.3838 nan 0.0100 0.9110
## 8 52.5337 nan 0.0100 0.8567
## 9 51.7094 nan 0.0100 0.8253
## 10 50.9043 nan 0.0100 0.8165
## 20 43.5253 nan 0.0100 0.5899
## 40 31.9691 nan 0.0100 0.4550
## 60 24.0826 nan 0.0100 0.3469
## 80 18.5107 nan 0.0100 0.2061
## 100 14.5277 nan 0.0100 0.1415
## 120 11.7414 nan 0.0100 0.1090
## 140 9.7198 nan 0.0100 0.0588
## 160 8.2472 nan 0.0100 0.0605
## 180 7.1113 nan 0.0100 0.0409
## 200 6.2665 nan 0.0100 0.0235
## 220 5.6294 nan 0.0100 0.0133
## 240 5.1207 nan 0.0100 0.0140
## 260 4.7755 nan 0.0100 0.0110
## 280 4.4774 nan 0.0100 0.0047
## 300 4.2470 nan 0.0100 0.0015
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 59.0213 nan 0.0100 1.0300
## 2 58.0681 nan 0.0100 1.0267
## 3 57.1088 nan 0.0100 0.8565
## 4 56.2438 nan 0.0100 0.9469
## 5 55.3183 nan 0.0100 0.9092
## 6 54.4064 nan 0.0100 0.9397
## 7 53.5457 nan 0.0100 0.8244
## 8 52.6850 nan 0.0100 0.7748
## 9 51.8533 nan 0.0100 0.7886
## 10 51.0059 nan 0.0100 0.7634
## 20 43.5978 nan 0.0100 0.6902
## 40 32.1357 nan 0.0100 0.4803
## 60 24.1292 nan 0.0100 0.2968
## 80 18.5851 nan 0.0100 0.2235
## 100 14.5994 nan 0.0100 0.1744
## 120 11.7401 nan 0.0100 0.1218
## 140 9.7248 nan 0.0100 0.1024
## 160 8.2642 nan 0.0100 0.0579
## 180 7.1507 nan 0.0100 0.0379
## 200 6.3387 nan 0.0100 0.0274
## 220 5.7445 nan 0.0100 0.0180
## 240 5.2660 nan 0.0100 0.0187
## 260 4.8890 nan 0.0100 0.0057
## 280 4.6056 nan 0.0100 0.0013
## 300 4.3881 nan 0.0100 0.0047
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 59.0303 nan 0.0100 0.9225
## 2 58.0235 nan 0.0100 0.9958
## 3 57.0225 nan 0.0100 0.9168
## 4 56.1281 nan 0.0100 0.8586
## 5 55.2039 nan 0.0100 0.9568
## 6 54.2922 nan 0.0100 0.9264
## 7 53.3621 nan 0.0100 0.8998
## 8 52.4992 nan 0.0100 0.7910
## 9 51.6467 nan 0.0100 0.8210
## 10 50.8060 nan 0.0100 0.7185
## 20 43.3211 nan 0.0100 0.6848
## 40 32.0377 nan 0.0100 0.5019
## 60 24.1766 nan 0.0100 0.3266
## 80 18.6001 nan 0.0100 0.2502
## 100 14.7534 nan 0.0100 0.1393
## 120 11.9649 nan 0.0100 0.0966
## 140 9.9451 nan 0.0100 0.0732
## 160 8.4430 nan 0.0100 0.0672
## 180 7.3253 nan 0.0100 0.0401
## 200 6.5429 nan 0.0100 0.0263
## 220 5.9051 nan 0.0100 0.0210
## 240 5.4338 nan 0.0100 0.0170
## 260 5.0842 nan 0.0100 0.0008
## 280 4.8305 nan 0.0100 0.0014
## 300 4.6088 nan 0.0100 0.0043
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 58.9433 nan 0.0100 1.0394
## 2 57.9199 nan 0.0100 0.9604
## 3 56.9376 nan 0.0100 0.9595
## 4 55.9689 nan 0.0100 0.9725
## 5 55.0147 nan 0.0100 0.9986
## 6 54.0169 nan 0.0100 0.9901
## 7 53.1426 nan 0.0100 0.8499
## 8 52.2802 nan 0.0100 0.9094
## 9 51.4344 nan 0.0100 0.9726
## 10 50.5472 nan 0.0100 0.7647
## 20 42.6206 nan 0.0100 0.6987
## 40 30.7592 nan 0.0100 0.4881
## 60 22.5728 nan 0.0100 0.3365
## 80 16.9905 nan 0.0100 0.2175
## 100 13.0073 nan 0.0100 0.1553
## 120 10.2943 nan 0.0100 0.0872
## 140 8.3465 nan 0.0100 0.0584
## 160 6.9901 nan 0.0100 0.0422
## 180 5.9816 nan 0.0100 0.0316
## 200 5.2494 nan 0.0100 0.0267
## 220 4.7119 nan 0.0100 0.0136
## 240 4.2793 nan 0.0100 0.0059
## 260 3.9677 nan 0.0100 0.0028
## 280 3.7206 nan 0.0100 -0.0032
## 300 3.5405 nan 0.0100 0.0030
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 58.9771 nan 0.0100 1.0138
## 2 57.9388 nan 0.0100 1.0215
## 3 56.9057 nan 0.0100 0.8507
## 4 55.9144 nan 0.0100 0.8979
## 5 54.9286 nan 0.0100 0.8803
## 6 54.0013 nan 0.0100 0.9204
## 7 53.0771 nan 0.0100 0.9776
## 8 52.1396 nan 0.0100 0.8768
## 9 51.2288 nan 0.0100 0.8359
## 10 50.3323 nan 0.0100 0.7550
## 20 42.4686 nan 0.0100 0.5999
## 40 30.6127 nan 0.0100 0.4739
## 60 22.5577 nan 0.0100 0.3430
## 80 16.9177 nan 0.0100 0.2607
## 100 12.9853 nan 0.0100 0.1590
## 120 10.2636 nan 0.0100 0.0974
## 140 8.3866 nan 0.0100 0.0750
## 160 7.0250 nan 0.0100 0.0379
## 180 6.0291 nan 0.0100 0.0290
## 200 5.3336 nan 0.0100 0.0175
## 220 4.8431 nan 0.0100 0.0155
## 240 4.4717 nan 0.0100 0.0085
## 260 4.1905 nan 0.0100 0.0039
## 280 3.9739 nan 0.0100 0.0021
## 300 3.8022 nan 0.0100 0.0016
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 58.9336 nan 0.0100 0.9507
## 2 57.8893 nan 0.0100 0.9064
## 3 56.8940 nan 0.0100 1.0292
## 4 55.8967 nan 0.0100 0.8574
## 5 54.9416 nan 0.0100 1.0302
## 6 53.9858 nan 0.0100 1.0444
## 7 53.0169 nan 0.0100 0.7872
## 8 52.1217 nan 0.0100 0.8675
## 9 51.2298 nan 0.0100 0.8214
## 10 50.3466 nan 0.0100 0.9471
## 20 42.3914 nan 0.0100 0.7441
## 40 30.5107 nan 0.0100 0.4414
## 60 22.5680 nan 0.0100 0.3294
## 80 17.0209 nan 0.0100 0.1983
## 100 13.1402 nan 0.0100 0.1475
## 120 10.4200 nan 0.0100 0.1067
## 140 8.5383 nan 0.0100 0.0598
## 160 7.1786 nan 0.0100 0.0489
## 180 6.2518 nan 0.0100 0.0351
## 200 5.5555 nan 0.0100 0.0257
## 220 5.0721 nan 0.0100 0.0197
## 240 4.7175 nan 0.0100 0.0119
## 260 4.4548 nan 0.0100 0.0058
## 280 4.2469 nan 0.0100 0.0015
## 300 4.0950 nan 0.0100 0.0043
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 56.5437 nan 0.0500 3.7098
## 2 53.0697 nan 0.0500 3.1212
## 3 49.9928 nan 0.0500 2.9125
## 4 47.1722 nan 0.0500 2.7779
## 5 44.9008 nan 0.0500 2.1183
## 6 42.6793 nan 0.0500 2.4575
## 7 40.5084 nan 0.0500 2.1310
## 8 38.3232 nan 0.0500 2.0163
## 9 36.7718 nan 0.0500 1.7894
## 10 34.8427 nan 0.0500 1.7069
## 20 22.3214 nan 0.0500 0.9520
## 40 11.4992 nan 0.0500 0.2846
## 60 7.5983 nan 0.0500 0.1164
## 80 5.7645 nan 0.0500 0.0518
## 100 4.9674 nan 0.0500 0.0154
## 120 4.5245 nan 0.0500 -0.0094
## 140 4.3442 nan 0.0500 -0.0069
## 160 4.2481 nan 0.0500 -0.0163
## 180 4.1385 nan 0.0500 -0.0010
## 200 4.0504 nan 0.0500 -0.0070
## 220 3.9910 nan 0.0500 -0.0034
## 240 3.9255 nan 0.0500 -0.0132
## 260 3.8697 nan 0.0500 0.0007
## 280 3.8276 nan 0.0500 -0.0178
## 300 3.7703 nan 0.0500 -0.0020
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 56.2519 nan 0.0500 3.6212
## 2 52.5945 nan 0.0500 3.3292
## 3 49.3846 nan 0.0500 2.7744
## 4 46.6029 nan 0.0500 2.5891
## 5 44.1908 nan 0.0500 2.4527
## 6 41.8764 nan 0.0500 2.1739
## 7 39.7937 nan 0.0500 2.1882
## 8 37.9501 nan 0.0500 1.7507
## 9 35.9900 nan 0.0500 1.7515
## 10 34.2726 nan 0.0500 1.7283
## 20 21.3945 nan 0.0500 0.8414
## 40 11.4588 nan 0.0500 0.2039
## 60 7.6137 nan 0.0500 0.0855
## 80 5.8009 nan 0.0500 0.0390
## 100 4.9844 nan 0.0500 0.0261
## 120 4.5739 nan 0.0500 0.0015
## 140 4.4265 nan 0.0500 -0.0165
## 160 4.3170 nan 0.0500 -0.0151
## 180 4.2502 nan 0.0500 -0.0170
## 200 4.1859 nan 0.0500 -0.0081
## 220 4.1156 nan 0.0500 -0.0113
## 240 4.0385 nan 0.0500 -0.0115
## 260 3.9913 nan 0.0500 -0.0214
## 280 3.9414 nan 0.0500 -0.0148
## 300 3.8936 nan 0.0500 -0.0116
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 56.1189 nan 0.0500 3.8541
## 2 52.6946 nan 0.0500 3.3061
## 3 49.7295 nan 0.0500 2.9384
## 4 47.2452 nan 0.0500 2.2361
## 5 44.5687 nan 0.0500 2.7725
## 6 41.9633 nan 0.0500 2.3470
## 7 39.8315 nan 0.0500 1.8245
## 8 37.7016 nan 0.0500 1.7331
## 9 35.6770 nan 0.0500 1.7630
## 10 33.8346 nan 0.0500 1.6129
## 20 21.3800 nan 0.0500 0.7759
## 40 11.2302 nan 0.0500 0.2535
## 60 7.3073 nan 0.0500 0.0740
## 80 5.6751 nan 0.0500 0.0505
## 100 4.9578 nan 0.0500 0.0039
## 120 4.6405 nan 0.0500 0.0044
## 140 4.4612 nan 0.0500 -0.0060
## 160 4.3504 nan 0.0500 -0.0033
## 180 4.2651 nan 0.0500 -0.0026
## 200 4.1977 nan 0.0500 -0.0060
## 220 4.1340 nan 0.0500 -0.0083
## 240 4.0835 nan 0.0500 -0.0074
## 260 4.0380 nan 0.0500 -0.0078
## 280 3.9891 nan 0.0500 -0.0203
## 300 3.9472 nan 0.0500 -0.0091
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 55.0031 nan 0.0500 4.7536
## 2 50.6186 nan 0.0500 4.6796
## 3 46.7006 nan 0.0500 3.6753
## 4 43.2462 nan 0.0500 3.6595
## 5 40.0116 nan 0.0500 3.1013
## 6 36.9215 nan 0.0500 3.1171
## 7 34.2902 nan 0.0500 2.6227
## 8 31.7084 nan 0.0500 2.7912
## 9 29.4480 nan 0.0500 1.8641
## 10 27.3565 nan 0.0500 2.0022
## 20 14.2739 nan 0.0500 0.7857
## 40 6.1140 nan 0.0500 0.1950
## 60 4.2708 nan 0.0500 0.0132
## 80 3.6362 nan 0.0500 -0.0039
## 100 3.3328 nan 0.0500 -0.0344
## 120 3.1261 nan 0.0500 -0.0336
## 140 2.9107 nan 0.0500 -0.0101
## 160 2.7323 nan 0.0500 -0.0076
## 180 2.5784 nan 0.0500 -0.0225
## 200 2.4596 nan 0.0500 -0.0221
## 220 2.3346 nan 0.0500 -0.0138
## 240 2.2000 nan 0.0500 -0.0090
## 260 2.1065 nan 0.0500 -0.0087
## 280 2.0111 nan 0.0500 -0.0090
## 300 1.9241 nan 0.0500 -0.0158
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 55.0596 nan 0.0500 4.8970
## 2 50.6709 nan 0.0500 4.6436
## 3 46.7853 nan 0.0500 3.8941
## 4 43.1507 nan 0.0500 3.7033
## 5 39.8694 nan 0.0500 3.5110
## 6 36.8854 nan 0.0500 3.0157
## 7 34.2227 nan 0.0500 2.3791
## 8 31.7756 nan 0.0500 2.4047
## 9 29.4824 nan 0.0500 2.2494
## 10 27.4525 nan 0.0500 1.8720
## 20 14.4102 nan 0.0500 0.7882
## 40 6.2311 nan 0.0500 0.1794
## 60 4.3526 nan 0.0500 0.0146
## 80 3.7797 nan 0.0500 -0.0049
## 100 3.4830 nan 0.0500 -0.0129
## 120 3.3334 nan 0.0500 -0.0170
## 140 3.1593 nan 0.0500 -0.0156
## 160 3.0000 nan 0.0500 -0.0076
## 180 2.8621 nan 0.0500 -0.0170
## 200 2.7677 nan 0.0500 -0.0134
## 220 2.6649 nan 0.0500 -0.0138
## 240 2.5697 nan 0.0500 -0.0183
## 260 2.4969 nan 0.0500 -0.0226
## 280 2.4077 nan 0.0500 -0.0170
## 300 2.3356 nan 0.0500 -0.0114
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 55.0833 nan 0.0500 4.8117
## 2 50.6656 nan 0.0500 4.2385
## 3 46.7431 nan 0.0500 3.8701
## 4 42.9569 nan 0.0500 3.5938
## 5 39.7451 nan 0.0500 3.1308
## 6 36.8169 nan 0.0500 3.0587
## 7 34.0997 nan 0.0500 2.5170
## 8 31.5950 nan 0.0500 2.2671
## 9 29.3643 nan 0.0500 2.1708
## 10 27.3067 nan 0.0500 1.9808
## 20 14.4779 nan 0.0500 0.7930
## 40 6.3575 nan 0.0500 0.1344
## 60 4.4701 nan 0.0500 0.0154
## 80 3.9765 nan 0.0500 -0.0194
## 100 3.7571 nan 0.0500 -0.0311
## 120 3.5849 nan 0.0500 -0.0210
## 140 3.4075 nan 0.0500 -0.0103
## 160 3.2624 nan 0.0500 -0.0202
## 180 3.1568 nan 0.0500 -0.0070
## 200 3.0477 nan 0.0500 -0.0056
## 220 2.9617 nan 0.0500 -0.0117
## 240 2.8730 nan 0.0500 -0.0242
## 260 2.7891 nan 0.0500 -0.0168
## 280 2.7071 nan 0.0500 -0.0239
## 300 2.6284 nan 0.0500 -0.0136
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 54.8492 nan 0.0500 4.5112
## 2 50.0878 nan 0.0500 4.1064
## 3 45.6928 nan 0.0500 3.4714
## 4 41.9608 nan 0.0500 3.0862
## 5 38.5289 nan 0.0500 3.8358
## 6 35.3583 nan 0.0500 3.2631
## 7 32.4365 nan 0.0500 3.0389
## 8 29.9451 nan 0.0500 2.1873
## 9 27.6902 nan 0.0500 2.3201
## 10 25.6727 nan 0.0500 1.8012
## 20 12.6098 nan 0.0500 0.6920
## 40 5.0594 nan 0.0500 0.1118
## 60 3.4778 nan 0.0500 0.0052
## 80 2.9385 nan 0.0500 -0.0361
## 100 2.5817 nan 0.0500 -0.0057
## 120 2.3473 nan 0.0500 -0.0182
## 140 2.1504 nan 0.0500 -0.0235
## 160 1.9689 nan 0.0500 -0.0140
## 180 1.8034 nan 0.0500 -0.0080
## 200 1.6642 nan 0.0500 -0.0089
## 220 1.5486 nan 0.0500 -0.0105
## 240 1.4579 nan 0.0500 -0.0100
## 260 1.3498 nan 0.0500 -0.0079
## 280 1.2615 nan 0.0500 -0.0116
## 300 1.1803 nan 0.0500 -0.0142
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 54.6374 nan 0.0500 5.1688
## 2 49.8232 nan 0.0500 4.8748
## 3 45.9219 nan 0.0500 3.8021
## 4 42.0953 nan 0.0500 3.7668
## 5 38.6995 nan 0.0500 3.4668
## 6 35.6279 nan 0.0500 3.2604
## 7 32.8248 nan 0.0500 2.7440
## 8 30.2593 nan 0.0500 2.3873
## 9 27.8219 nan 0.0500 2.0921
## 10 25.7640 nan 0.0500 2.2734
## 20 12.8661 nan 0.0500 0.8644
## 40 5.3059 nan 0.0500 0.0879
## 60 3.8528 nan 0.0500 0.0108
## 80 3.3234 nan 0.0500 -0.0208
## 100 3.0574 nan 0.0500 -0.0395
## 120 2.8417 nan 0.0500 -0.0215
## 140 2.6546 nan 0.0500 -0.0343
## 160 2.4573 nan 0.0500 -0.0258
## 180 2.2995 nan 0.0500 -0.0151
## 200 2.1688 nan 0.0500 -0.0176
## 220 2.0336 nan 0.0500 -0.0122
## 240 1.9357 nan 0.0500 -0.0201
## 260 1.8412 nan 0.0500 -0.0160
## 280 1.7303 nan 0.0500 -0.0092
## 300 1.6556 nan 0.0500 -0.0152
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 55.0788 nan 0.0500 4.7070
## 2 50.7048 nan 0.0500 4.2124
## 3 46.5665 nan 0.0500 3.8875
## 4 42.6249 nan 0.0500 3.9539
## 5 39.2931 nan 0.0500 3.2314
## 6 36.2105 nan 0.0500 2.8375
## 7 33.1810 nan 0.0500 2.7027
## 8 30.8120 nan 0.0500 2.3923
## 9 28.5429 nan 0.0500 2.4052
## 10 26.1687 nan 0.0500 1.9534
## 20 13.0188 nan 0.0500 0.8343
## 40 5.5594 nan 0.0500 0.1089
## 60 4.1702 nan 0.0500 -0.0218
## 80 3.7304 nan 0.0500 -0.0354
## 100 3.4427 nan 0.0500 -0.0104
## 120 3.2040 nan 0.0500 -0.0182
## 140 3.0432 nan 0.0500 -0.0142
## 160 2.8790 nan 0.0500 -0.0158
## 180 2.7583 nan 0.0500 -0.0244
## 200 2.6451 nan 0.0500 -0.0167
## 220 2.5285 nan 0.0500 -0.0052
## 240 2.4306 nan 0.0500 -0.0070
## 260 2.3596 nan 0.0500 -0.0139
## 280 2.2816 nan 0.0500 -0.0134
## 300 2.1927 nan 0.0500 -0.0092
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 53.5281 nan 0.1000 7.2479
## 2 47.8149 nan 0.1000 5.5422
## 3 42.4283 nan 0.1000 4.6154
## 4 37.8311 nan 0.1000 3.8128
## 5 34.1432 nan 0.1000 3.2938
## 6 30.9308 nan 0.1000 3.0563
## 7 28.1967 nan 0.1000 2.8445
## 8 25.7157 nan 0.1000 2.2590
## 9 23.5325 nan 0.1000 2.2328
## 10 21.5697 nan 0.1000 1.5910
## 20 11.4103 nan 0.1000 0.4502
## 40 5.8908 nan 0.1000 0.0649
## 60 4.7470 nan 0.1000 0.0244
## 80 4.4876 nan 0.1000 -0.0195
## 100 4.2819 nan 0.1000 -0.0303
## 120 4.1210 nan 0.1000 -0.0486
## 140 4.0010 nan 0.1000 -0.0140
## 160 3.9178 nan 0.1000 -0.0038
## 180 3.8191 nan 0.1000 -0.0033
## 200 3.7323 nan 0.1000 -0.0178
## 220 3.6466 nan 0.1000 -0.0273
## 240 3.5952 nan 0.1000 -0.0385
## 260 3.5568 nan 0.1000 -0.0101
## 280 3.4850 nan 0.1000 -0.0117
## 300 3.4171 nan 0.1000 -0.0290
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 52.4688 nan 0.1000 6.8355
## 2 46.7668 nan 0.1000 5.6443
## 3 42.0435 nan 0.1000 3.7916
## 4 37.6993 nan 0.1000 4.1359
## 5 33.9098 nan 0.1000 3.6237
## 6 30.6766 nan 0.1000 3.0740
## 7 28.0555 nan 0.1000 2.6132
## 8 25.7755 nan 0.1000 2.3319
## 9 23.5610 nan 0.1000 2.1597
## 10 21.7994 nan 0.1000 1.4807
## 20 11.2909 nan 0.1000 0.4523
## 40 5.7716 nan 0.1000 0.1114
## 60 4.6718 nan 0.1000 0.0014
## 80 4.4266 nan 0.1000 -0.0225
## 100 4.2606 nan 0.1000 -0.0082
## 120 4.1296 nan 0.1000 -0.0291
## 140 4.0137 nan 0.1000 -0.0248
## 160 3.9415 nan 0.1000 -0.0137
## 180 3.8712 nan 0.1000 -0.0206
## 200 3.7952 nan 0.1000 -0.0216
## 220 3.7414 nan 0.1000 -0.0157
## 240 3.6685 nan 0.1000 -0.0029
## 260 3.6078 nan 0.1000 -0.0007
## 280 3.5374 nan 0.1000 -0.0101
## 300 3.4830 nan 0.1000 -0.0273
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 52.5100 nan 0.1000 7.3293
## 2 47.0078 nan 0.1000 4.4970
## 3 41.5680 nan 0.1000 5.0181
## 4 37.8943 nan 0.1000 2.9780
## 5 34.0442 nan 0.1000 3.8521
## 6 30.6512 nan 0.1000 3.3069
## 7 28.0318 nan 0.1000 2.5961
## 8 25.5893 nan 0.1000 2.3818
## 9 23.6862 nan 0.1000 1.9288
## 10 21.7993 nan 0.1000 1.6415
## 20 11.1761 nan 0.1000 0.4423
## 40 5.8680 nan 0.1000 0.0417
## 60 4.8751 nan 0.1000 0.0218
## 80 4.6029 nan 0.1000 -0.0001
## 100 4.4036 nan 0.1000 -0.0353
## 120 4.3228 nan 0.1000 -0.0226
## 140 4.2011 nan 0.1000 -0.0193
## 160 4.0845 nan 0.1000 -0.0070
## 180 4.0007 nan 0.1000 -0.0113
## 200 3.9276 nan 0.1000 -0.0088
## 220 3.8578 nan 0.1000 -0.0142
## 240 3.7646 nan 0.1000 -0.0167
## 260 3.7279 nan 0.1000 -0.0070
## 280 3.6619 nan 0.1000 -0.0199
## 300 3.6180 nan 0.1000 -0.0356
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 50.5569 nan 0.1000 8.9914
## 2 42.9705 nan 0.1000 8.5212
## 3 36.2586 nan 0.1000 6.2291
## 4 30.9603 nan 0.1000 4.9982
## 5 26.5420 nan 0.1000 3.8534
## 6 23.2380 nan 0.1000 3.4850
## 7 20.2497 nan 0.1000 2.9528
## 8 17.7105 nan 0.1000 2.4129
## 9 15.7135 nan 0.1000 1.7885
## 10 13.9571 nan 0.1000 1.5724
## 20 6.1086 nan 0.1000 0.2061
## 40 3.6734 nan 0.1000 -0.0460
## 60 3.1664 nan 0.1000 -0.0425
## 80 2.7902 nan 0.1000 -0.0377
## 100 2.4625 nan 0.1000 -0.0293
## 120 2.2336 nan 0.1000 -0.0352
## 140 2.0254 nan 0.1000 -0.0328
## 160 1.8481 nan 0.1000 -0.0248
## 180 1.7119 nan 0.1000 -0.0222
## 200 1.5902 nan 0.1000 -0.0182
## 220 1.4940 nan 0.1000 -0.0340
## 240 1.3882 nan 0.1000 -0.0247
## 260 1.3119 nan 0.1000 -0.0136
## 280 1.2483 nan 0.1000 -0.0200
## 300 1.1720 nan 0.1000 -0.0120
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 50.3462 nan 0.1000 10.0493
## 2 42.8392 nan 0.1000 7.9173
## 3 36.4507 nan 0.1000 6.1877
## 4 31.0251 nan 0.1000 5.7378
## 5 26.7595 nan 0.1000 4.2033
## 6 23.2069 nan 0.1000 3.7299
## 7 20.3183 nan 0.1000 2.2430
## 8 17.9982 nan 0.1000 2.5113
## 9 15.9327 nan 0.1000 1.9216
## 10 13.9819 nan 0.1000 1.9531
## 20 6.0913 nan 0.1000 0.3516
## 40 3.8748 nan 0.1000 -0.0575
## 60 3.3743 nan 0.1000 -0.0592
## 80 3.0458 nan 0.1000 -0.0129
## 100 2.8265 nan 0.1000 -0.0392
## 120 2.6353 nan 0.1000 -0.0324
## 140 2.4789 nan 0.1000 -0.0281
## 160 2.3258 nan 0.1000 -0.0157
## 180 2.1810 nan 0.1000 -0.0343
## 200 2.0515 nan 0.1000 -0.0331
## 220 1.9500 nan 0.1000 -0.0103
## 240 1.8550 nan 0.1000 -0.0134
## 260 1.7561 nan 0.1000 -0.0267
## 280 1.6364 nan 0.1000 -0.0169
## 300 1.5617 nan 0.1000 -0.0173
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 50.6671 nan 0.1000 9.9589
## 2 42.7561 nan 0.1000 7.9993
## 3 36.2836 nan 0.1000 6.1796
## 4 31.1824 nan 0.1000 5.3339
## 5 26.8244 nan 0.1000 3.8962
## 6 23.0563 nan 0.1000 3.4314
## 7 20.3664 nan 0.1000 2.1878
## 8 17.9298 nan 0.1000 2.2807
## 9 15.8261 nan 0.1000 2.1278
## 10 13.9396 nan 0.1000 1.6152
## 20 6.2854 nan 0.1000 0.3411
## 40 4.0349 nan 0.1000 -0.0027
## 60 3.5577 nan 0.1000 -0.0394
## 80 3.3283 nan 0.1000 -0.0447
## 100 3.1081 nan 0.1000 -0.0310
## 120 2.9438 nan 0.1000 -0.0208
## 140 2.7790 nan 0.1000 -0.0228
## 160 2.6710 nan 0.1000 -0.0521
## 180 2.5232 nan 0.1000 -0.0441
## 200 2.4025 nan 0.1000 -0.0267
## 220 2.3108 nan 0.1000 -0.0054
## 240 2.2322 nan 0.1000 -0.0334
## 260 2.1119 nan 0.1000 -0.0159
## 280 2.0132 nan 0.1000 -0.0230
## 300 1.9296 nan 0.1000 -0.0186
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 50.0617 nan 0.1000 10.7192
## 2 41.9580 nan 0.1000 7.5755
## 3 35.6701 nan 0.1000 6.1662
## 4 30.5153 nan 0.1000 4.6316
## 5 25.9751 nan 0.1000 4.5299
## 6 22.3096 nan 0.1000 3.3097
## 7 18.9979 nan 0.1000 2.9207
## 8 16.4853 nan 0.1000 2.2905
## 9 14.1806 nan 0.1000 2.1583
## 10 12.5830 nan 0.1000 1.6320
## 20 5.1973 nan 0.1000 0.1760
## 40 3.1049 nan 0.1000 -0.0290
## 60 2.4424 nan 0.1000 -0.0539
## 80 2.0284 nan 0.1000 -0.0248
## 100 1.7115 nan 0.1000 -0.0156
## 120 1.4794 nan 0.1000 -0.0230
## 140 1.2833 nan 0.1000 -0.0102
## 160 1.1413 nan 0.1000 -0.0221
## 180 1.0078 nan 0.1000 -0.0131
## 200 0.8872 nan 0.1000 -0.0111
## 220 0.8038 nan 0.1000 -0.0132
## 240 0.7118 nan 0.1000 -0.0111
## 260 0.6464 nan 0.1000 -0.0119
## 280 0.5772 nan 0.1000 -0.0092
## 300 0.5311 nan 0.1000 -0.0098
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 50.3042 nan 0.1000 10.2468
## 2 42.2105 nan 0.1000 7.9157
## 3 35.3382 nan 0.1000 6.5465
## 4 29.6980 nan 0.1000 5.4504
## 5 25.4972 nan 0.1000 4.1565
## 6 22.0284 nan 0.1000 3.3164
## 7 18.9599 nan 0.1000 2.7861
## 8 16.4990 nan 0.1000 2.4939
## 9 14.3586 nan 0.1000 2.1044
## 10 12.4830 nan 0.1000 1.6987
## 20 5.0833 nan 0.1000 0.1616
## 40 3.3004 nan 0.1000 -0.0661
## 60 2.7950 nan 0.1000 -0.0718
## 80 2.4609 nan 0.1000 -0.0481
## 100 2.1921 nan 0.1000 -0.0158
## 120 1.9888 nan 0.1000 -0.0496
## 140 1.8065 nan 0.1000 -0.0207
## 160 1.6749 nan 0.1000 -0.0121
## 180 1.5159 nan 0.1000 -0.0223
## 200 1.3948 nan 0.1000 -0.0241
## 220 1.3063 nan 0.1000 -0.0282
## 240 1.1998 nan 0.1000 -0.0258
## 260 1.1139 nan 0.1000 -0.0114
## 280 1.0310 nan 0.1000 -0.0140
## 300 0.9670 nan 0.1000 -0.0196
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 50.2072 nan 0.1000 10.7663
## 2 42.1874 nan 0.1000 8.2551
## 3 35.5826 nan 0.1000 6.8193
## 4 30.0732 nan 0.1000 5.0494
## 5 25.3116 nan 0.1000 3.8473
## 6 21.6925 nan 0.1000 3.3290
## 7 18.6747 nan 0.1000 2.8151
## 8 16.1471 nan 0.1000 2.4604
## 9 13.8891 nan 0.1000 2.2895
## 10 12.1598 nan 0.1000 1.6660
## 20 5.2250 nan 0.1000 0.2167
## 40 3.6210 nan 0.1000 -0.0366
## 60 3.1802 nan 0.1000 -0.0213
## 80 2.8731 nan 0.1000 -0.0365
## 100 2.6212 nan 0.1000 -0.0467
## 120 2.4340 nan 0.1000 -0.0312
## 140 2.2588 nan 0.1000 -0.0316
## 160 2.0799 nan 0.1000 -0.0425
## 180 1.9716 nan 0.1000 -0.0153
## 200 1.8239 nan 0.1000 -0.0262
## 220 1.7260 nan 0.1000 -0.0316
## 240 1.6443 nan 0.1000 -0.0459
## 260 1.5428 nan 0.1000 -0.0245
## 280 1.4481 nan 0.1000 -0.0252
## 300 1.3750 nan 0.1000 -0.0236
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 60.7660 nan 0.0100 0.7927
## 2 59.9760 nan 0.0100 0.7477
## 3 59.2460 nan 0.0100 0.7661
## 4 58.5057 nan 0.0100 0.7125
## 5 57.7785 nan 0.0100 0.6982
## 6 57.0185 nan 0.0100 0.6674
## 7 56.2991 nan 0.0100 0.6669
## 8 55.6880 nan 0.0100 0.6322
## 9 55.0246 nan 0.0100 0.6155
## 10 54.3761 nan 0.0100 0.6360
## 20 48.7411 nan 0.0100 0.4891
## 40 39.4304 nan 0.0100 0.3750
## 60 32.2577 nan 0.0100 0.3153
## 80 26.7796 nan 0.0100 0.2220
## 100 22.7368 nan 0.0100 0.1573
## 120 19.5698 nan 0.0100 0.1236
## 140 17.0398 nan 0.0100 0.0929
## 160 15.0214 nan 0.0100 0.0786
## 180 13.4291 nan 0.0100 0.0611
## 200 12.0654 nan 0.0100 0.0583
## 220 10.9251 nan 0.0100 0.0325
## 240 9.9583 nan 0.0100 0.0414
## 260 9.1621 nan 0.0100 0.0304
## 280 8.4568 nan 0.0100 0.0284
## 300 7.8666 nan 0.0100 0.0126
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 60.7870 nan 0.0100 0.7521
## 2 59.9866 nan 0.0100 0.7324
## 3 59.2573 nan 0.0100 0.7847
## 4 58.5858 nan 0.0100 0.7678
## 5 57.8359 nan 0.0100 0.7086
## 6 57.1980 nan 0.0100 0.6928
## 7 56.5639 nan 0.0100 0.6651
## 8 55.8827 nan 0.0100 0.6671
## 9 55.2338 nan 0.0100 0.6655
## 10 54.5715 nan 0.0100 0.6858
## 20 48.6301 nan 0.0100 0.5175
## 40 39.4264 nan 0.0100 0.3907
## 60 32.2260 nan 0.0100 0.2737
## 80 26.8744 nan 0.0100 0.2050
## 100 22.8219 nan 0.0100 0.1605
## 120 19.6324 nan 0.0100 0.1127
## 140 17.0758 nan 0.0100 0.1062
## 160 15.0522 nan 0.0100 0.0783
## 180 13.4349 nan 0.0100 0.0576
## 200 12.0865 nan 0.0100 0.0231
## 220 10.9397 nan 0.0100 0.0408
## 240 9.9785 nan 0.0100 0.0353
## 260 9.1596 nan 0.0100 0.0242
## 280 8.5025 nan 0.0100 0.0225
## 300 7.9023 nan 0.0100 0.0253
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 60.8056 nan 0.0100 0.7570
## 2 59.9650 nan 0.0100 0.7216
## 3 59.2376 nan 0.0100 0.6620
## 4 58.5428 nan 0.0100 0.7071
## 5 57.8593 nan 0.0100 0.6982
## 6 57.1008 nan 0.0100 0.7085
## 7 56.4487 nan 0.0100 0.6525
## 8 55.7938 nan 0.0100 0.6292
## 9 55.1709 nan 0.0100 0.6243
## 10 54.4801 nan 0.0100 0.6401
## 20 48.7284 nan 0.0100 0.5626
## 40 39.4472 nan 0.0100 0.3906
## 60 32.5514 nan 0.0100 0.2997
## 80 27.1401 nan 0.0100 0.2148
## 100 22.9807 nan 0.0100 0.1464
## 120 19.8094 nan 0.0100 0.1218
## 140 17.2751 nan 0.0100 0.1043
## 160 15.2594 nan 0.0100 0.0786
## 180 13.5752 nan 0.0100 0.0567
## 200 12.1654 nan 0.0100 0.0432
## 220 11.0535 nan 0.0100 0.0412
## 240 10.1118 nan 0.0100 0.0383
## 260 9.3100 nan 0.0100 0.0279
## 280 8.6686 nan 0.0100 0.0156
## 300 8.0720 nan 0.0100 0.0240
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 60.6128 nan 0.0100 1.0022
## 2 59.6328 nan 0.0100 0.9715
## 3 58.6518 nan 0.0100 0.8832
## 4 57.6960 nan 0.0100 0.9816
## 5 56.8173 nan 0.0100 0.9529
## 6 55.9287 nan 0.0100 0.7488
## 7 55.0415 nan 0.0100 0.8416
## 8 54.1264 nan 0.0100 0.8460
## 9 53.2894 nan 0.0100 0.8406
## 10 52.4748 nan 0.0100 0.9753
## 20 44.7402 nan 0.0100 0.7383
## 40 33.0959 nan 0.0100 0.4822
## 60 24.9887 nan 0.0100 0.3277
## 80 19.1918 nan 0.0100 0.2214
## 100 15.1476 nan 0.0100 0.1284
## 120 12.2353 nan 0.0100 0.0843
## 140 10.0538 nan 0.0100 0.0946
## 160 8.4775 nan 0.0100 0.0641
## 180 7.2453 nan 0.0100 0.0427
## 200 6.3517 nan 0.0100 0.0266
## 220 5.6713 nan 0.0100 0.0245
## 240 5.1686 nan 0.0100 0.0153
## 260 4.7650 nan 0.0100 0.0105
## 280 4.4646 nan 0.0100 0.0067
## 300 4.2264 nan 0.0100 0.0029
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 60.5423 nan 0.0100 1.0307
## 2 59.5733 nan 0.0100 0.8742
## 3 58.5631 nan 0.0100 0.9935
## 4 57.6165 nan 0.0100 0.9033
## 5 56.7044 nan 0.0100 0.9568
## 6 55.7388 nan 0.0100 0.9210
## 7 54.8304 nan 0.0100 0.8119
## 8 53.9934 nan 0.0100 0.9096
## 9 53.1347 nan 0.0100 0.8593
## 10 52.3063 nan 0.0100 0.8568
## 20 44.8563 nan 0.0100 0.7231
## 40 32.9925 nan 0.0100 0.5209
## 60 24.9360 nan 0.0100 0.3016
## 80 19.1590 nan 0.0100 0.2231
## 100 15.1536 nan 0.0100 0.1545
## 120 12.2133 nan 0.0100 0.1124
## 140 10.0810 nan 0.0100 0.0798
## 160 8.4968 nan 0.0100 0.0561
## 180 7.3302 nan 0.0100 0.0431
## 200 6.4736 nan 0.0100 0.0340
## 220 5.8020 nan 0.0100 0.0259
## 240 5.2909 nan 0.0100 0.0189
## 260 4.9316 nan 0.0100 0.0145
## 280 4.6331 nan 0.0100 0.0074
## 300 4.3976 nan 0.0100 0.0014
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 60.5356 nan 0.0100 1.0743
## 2 59.5432 nan 0.0100 0.9617
## 3 58.6154 nan 0.0100 0.8984
## 4 57.6770 nan 0.0100 0.8789
## 5 56.7191 nan 0.0100 0.8860
## 6 55.8204 nan 0.0100 0.8711
## 7 54.8951 nan 0.0100 0.8866
## 8 54.0114 nan 0.0100 0.8630
## 9 53.1628 nan 0.0100 0.8181
## 10 52.3114 nan 0.0100 0.7424
## 20 44.6154 nan 0.0100 0.7551
## 40 33.0485 nan 0.0100 0.4651
## 60 24.9674 nan 0.0100 0.3414
## 80 19.2844 nan 0.0100 0.2349
## 100 15.2722 nan 0.0100 0.1593
## 120 12.3756 nan 0.0100 0.0879
## 140 10.2981 nan 0.0100 0.0777
## 160 8.7805 nan 0.0100 0.0578
## 180 7.6221 nan 0.0100 0.0443
## 200 6.7545 nan 0.0100 0.0296
## 220 6.1149 nan 0.0100 0.0198
## 240 5.6238 nan 0.0100 0.0135
## 260 5.2662 nan 0.0100 0.0097
## 280 4.9687 nan 0.0100 0.0059
## 300 4.7149 nan 0.0100 0.0010
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 60.5280 nan 0.0100 1.0017
## 2 59.4748 nan 0.0100 0.9913
## 3 58.3938 nan 0.0100 0.9527
## 4 57.4358 nan 0.0100 1.0419
## 5 56.3800 nan 0.0100 0.9905
## 6 55.4186 nan 0.0100 0.9519
## 7 54.4388 nan 0.0100 0.9300
## 8 53.5170 nan 0.0100 0.8389
## 9 52.5851 nan 0.0100 0.8512
## 10 51.7152 nan 0.0100 0.9048
## 20 43.6651 nan 0.0100 0.7197
## 40 31.5091 nan 0.0100 0.4966
## 60 23.0466 nan 0.0100 0.3513
## 80 17.2732 nan 0.0100 0.2402
## 100 13.2369 nan 0.0100 0.1814
## 120 10.3737 nan 0.0100 0.0978
## 140 8.3112 nan 0.0100 0.0581
## 160 6.9222 nan 0.0100 0.0510
## 180 5.9136 nan 0.0100 0.0315
## 200 5.1603 nan 0.0100 0.0218
## 220 4.6269 nan 0.0100 0.0170
## 240 4.1995 nan 0.0100 0.0151
## 260 3.8660 nan 0.0100 0.0042
## 280 3.6156 nan 0.0100 -0.0011
## 300 3.4135 nan 0.0100 0.0016
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 60.4604 nan 0.0100 0.9130
## 2 59.4163 nan 0.0100 1.0636
## 3 58.3742 nan 0.0100 0.9256
## 4 57.3522 nan 0.0100 0.9604
## 5 56.3652 nan 0.0100 1.0048
## 6 55.4290 nan 0.0100 0.8571
## 7 54.4483 nan 0.0100 0.9713
## 8 53.5343 nan 0.0100 0.8623
## 9 52.6204 nan 0.0100 0.8903
## 10 51.7416 nan 0.0100 0.9246
## 20 43.7414 nan 0.0100 0.7379
## 40 31.4345 nan 0.0100 0.4721
## 60 23.0673 nan 0.0100 0.3276
## 80 17.1852 nan 0.0100 0.1959
## 100 13.2600 nan 0.0100 0.1481
## 120 10.4380 nan 0.0100 0.1210
## 140 8.5035 nan 0.0100 0.0816
## 160 7.1124 nan 0.0100 0.0507
## 180 6.0979 nan 0.0100 0.0300
## 200 5.3779 nan 0.0100 0.0110
## 220 4.8405 nan 0.0100 0.0163
## 240 4.4333 nan 0.0100 0.0071
## 260 4.1369 nan 0.0100 0.0021
## 280 3.9018 nan 0.0100 -0.0008
## 300 3.7187 nan 0.0100 -0.0017
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 60.4956 nan 0.0100 1.1134
## 2 59.4575 nan 0.0100 0.9308
## 3 58.4299 nan 0.0100 0.9929
## 4 57.4287 nan 0.0100 0.8407
## 5 56.4436 nan 0.0100 1.0395
## 6 55.4575 nan 0.0100 0.9833
## 7 54.5460 nan 0.0100 0.9485
## 8 53.6878 nan 0.0100 0.9528
## 9 52.7938 nan 0.0100 0.9172
## 10 51.9040 nan 0.0100 0.8074
## 20 43.8624 nan 0.0100 0.7445
## 40 31.8083 nan 0.0100 0.4275
## 60 23.4099 nan 0.0100 0.2895
## 80 17.5890 nan 0.0100 0.2429
## 100 13.6808 nan 0.0100 0.1471
## 120 10.8663 nan 0.0100 0.0977
## 140 8.8851 nan 0.0100 0.0708
## 160 7.4761 nan 0.0100 0.0551
## 180 6.4844 nan 0.0100 0.0395
## 200 5.7690 nan 0.0100 0.0272
## 220 5.2517 nan 0.0100 0.0132
## 240 4.8505 nan 0.0100 0.0116
## 260 4.5769 nan 0.0100 0.0029
## 280 4.3490 nan 0.0100 0.0079
## 300 4.1635 nan 0.0100 -0.0003
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 57.7015 nan 0.0500 3.4982
## 2 54.2418 nan 0.0500 3.1776
## 3 51.0148 nan 0.0500 2.7234
## 4 48.1848 nan 0.0500 2.8085
## 5 45.9234 nan 0.0500 2.3217
## 6 43.5831 nan 0.0500 2.4684
## 7 41.5490 nan 0.0500 1.7567
## 8 39.5428 nan 0.0500 1.8231
## 9 37.6141 nan 0.0500 2.1494
## 10 35.7790 nan 0.0500 1.7716
## 20 22.9533 nan 0.0500 0.9134
## 40 12.2589 nan 0.0500 0.2651
## 60 8.1055 nan 0.0500 0.1218
## 80 6.0937 nan 0.0500 0.0217
## 100 5.2074 nan 0.0500 -0.0067
## 120 4.7190 nan 0.0500 -0.0083
## 140 4.4785 nan 0.0500 -0.0069
## 160 4.3330 nan 0.0500 -0.0086
## 180 4.2576 nan 0.0500 -0.0184
## 200 4.1562 nan 0.0500 -0.0269
## 220 4.0912 nan 0.0500 -0.0132
## 240 4.0271 nan 0.0500 -0.0104
## 260 3.9630 nan 0.0500 -0.0184
## 280 3.9027 nan 0.0500 -0.0027
## 300 3.8374 nan 0.0500 -0.0206
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 57.5794 nan 0.0500 3.7992
## 2 54.3230 nan 0.0500 3.2092
## 3 50.9122 nan 0.0500 3.2266
## 4 48.0505 nan 0.0500 2.5112
## 5 45.4481 nan 0.0500 2.2672
## 6 43.0324 nan 0.0500 2.2788
## 7 40.7323 nan 0.0500 1.9265
## 8 38.7387 nan 0.0500 1.9601
## 9 36.7217 nan 0.0500 1.9000
## 10 34.8995 nan 0.0500 1.6411
## 20 22.4937 nan 0.0500 0.9089
## 40 11.8917 nan 0.0500 0.2149
## 60 7.7633 nan 0.0500 0.1225
## 80 5.9714 nan 0.0500 0.0120
## 100 5.0798 nan 0.0500 -0.0101
## 120 4.7007 nan 0.0500 0.0081
## 140 4.4883 nan 0.0500 -0.0114
## 160 4.3417 nan 0.0500 -0.0147
## 180 4.2465 nan 0.0500 -0.0115
## 200 4.1704 nan 0.0500 -0.0351
## 220 4.1005 nan 0.0500 -0.0115
## 240 4.0413 nan 0.0500 -0.0055
## 260 3.9864 nan 0.0500 -0.0031
## 280 3.9366 nan 0.0500 -0.0141
## 300 3.8887 nan 0.0500 -0.0218
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 57.7586 nan 0.0500 3.5394
## 2 54.2993 nan 0.0500 3.7989
## 3 50.9941 nan 0.0500 2.9821
## 4 48.2152 nan 0.0500 2.4799
## 5 45.5350 nan 0.0500 2.4753
## 6 43.1732 nan 0.0500 2.1965
## 7 40.9369 nan 0.0500 2.2222
## 8 38.7347 nan 0.0500 2.0183
## 9 37.0822 nan 0.0500 1.7347
## 10 35.2522 nan 0.0500 1.6259
## 20 22.4820 nan 0.0500 0.8752
## 40 11.9974 nan 0.0500 0.2520
## 60 8.0188 nan 0.0500 0.0830
## 80 6.2073 nan 0.0500 0.0574
## 100 5.3636 nan 0.0500 0.0242
## 120 4.9926 nan 0.0500 0.0054
## 140 4.7710 nan 0.0500 -0.0054
## 160 4.6372 nan 0.0500 -0.0150
## 180 4.4956 nan 0.0500 -0.0032
## 200 4.4152 nan 0.0500 -0.0095
## 220 4.3268 nan 0.0500 -0.0298
## 240 4.2491 nan 0.0500 -0.0035
## 260 4.1822 nan 0.0500 -0.0180
## 280 4.1274 nan 0.0500 -0.0079
## 300 4.0693 nan 0.0500 -0.0078
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 56.7125 nan 0.0500 4.8834
## 2 52.0471 nan 0.0500 4.5408
## 3 48.0224 nan 0.0500 3.6153
## 4 44.5774 nan 0.0500 3.6362
## 5 41.1548 nan 0.0500 3.6088
## 6 38.1180 nan 0.0500 3.0381
## 7 35.3822 nan 0.0500 2.8435
## 8 32.8657 nan 0.0500 2.2436
## 9 30.5260 nan 0.0500 2.3342
## 10 28.4272 nan 0.0500 1.9290
## 20 14.7456 nan 0.0500 0.7583
## 40 6.2283 nan 0.0500 0.1508
## 60 4.2450 nan 0.0500 0.0093
## 80 3.5616 nan 0.0500 -0.0029
## 100 3.1795 nan 0.0500 -0.0228
## 120 2.9053 nan 0.0500 -0.0146
## 140 2.6717 nan 0.0500 -0.0094
## 160 2.5057 nan 0.0500 -0.0022
## 180 2.3555 nan 0.0500 -0.0145
## 200 2.2091 nan 0.0500 -0.0110
## 220 2.0794 nan 0.0500 -0.0099
## 240 1.9680 nan 0.0500 -0.0180
## 260 1.8718 nan 0.0500 -0.0193
## 280 1.7793 nan 0.0500 -0.0071
## 300 1.7130 nan 0.0500 -0.0149
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 56.7490 nan 0.0500 4.6916
## 2 52.3650 nan 0.0500 4.6628
## 3 48.4174 nan 0.0500 3.9909
## 4 44.7935 nan 0.0500 3.4565
## 5 41.4009 nan 0.0500 3.0197
## 6 38.2488 nan 0.0500 2.7600
## 7 35.3945 nan 0.0500 2.7726
## 8 32.6785 nan 0.0500 2.6216
## 9 30.4299 nan 0.0500 2.2168
## 10 28.2481 nan 0.0500 2.0930
## 20 14.7523 nan 0.0500 0.8113
## 40 6.4072 nan 0.0500 0.1383
## 60 4.3777 nan 0.0500 -0.0053
## 80 3.7216 nan 0.0500 -0.0066
## 100 3.3925 nan 0.0500 -0.0137
## 120 3.1470 nan 0.0500 -0.0176
## 140 2.9600 nan 0.0500 -0.0195
## 160 2.8231 nan 0.0500 -0.0119
## 180 2.6784 nan 0.0500 -0.0159
## 200 2.5609 nan 0.0500 -0.0174
## 220 2.4551 nan 0.0500 -0.0076
## 240 2.3622 nan 0.0500 -0.0143
## 260 2.2791 nan 0.0500 -0.0149
## 280 2.1983 nan 0.0500 -0.0199
## 300 2.1106 nan 0.0500 -0.0137
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 56.8504 nan 0.0500 5.5487
## 2 52.3049 nan 0.0500 4.3477
## 3 48.1635 nan 0.0500 4.0110
## 4 44.4085 nan 0.0500 3.7169
## 5 41.2354 nan 0.0500 3.0825
## 6 38.0796 nan 0.0500 2.9684
## 7 35.1033 nan 0.0500 2.6028
## 8 32.6581 nan 0.0500 2.4002
## 9 30.3600 nan 0.0500 2.2336
## 10 28.4114 nan 0.0500 2.1210
## 20 15.0378 nan 0.0500 0.7062
## 40 6.8280 nan 0.0500 0.1544
## 60 4.8615 nan 0.0500 0.0122
## 80 4.2513 nan 0.0500 -0.0209
## 100 3.8814 nan 0.0500 -0.0027
## 120 3.6592 nan 0.0500 -0.0246
## 140 3.4690 nan 0.0500 -0.0126
## 160 3.2878 nan 0.0500 0.0015
## 180 3.1510 nan 0.0500 -0.0087
## 200 3.0242 nan 0.0500 -0.0176
## 220 2.9191 nan 0.0500 -0.0308
## 240 2.8278 nan 0.0500 -0.0117
## 260 2.7560 nan 0.0500 -0.0069
## 280 2.6777 nan 0.0500 -0.0183
## 300 2.6141 nan 0.0500 -0.0129
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 56.2638 nan 0.0500 4.9113
## 2 51.4327 nan 0.0500 5.1917
## 3 47.1931 nan 0.0500 4.5336
## 4 43.3367 nan 0.0500 3.5749
## 5 39.7113 nan 0.0500 3.3623
## 6 36.5267 nan 0.0500 3.0600
## 7 33.6145 nan 0.0500 2.9483
## 8 30.9439 nan 0.0500 2.4926
## 9 28.6520 nan 0.0500 2.3665
## 10 26.3901 nan 0.0500 2.0501
## 20 12.7373 nan 0.0500 0.6948
## 40 5.1322 nan 0.0500 0.1296
## 60 3.3519 nan 0.0500 -0.0029
## 80 2.7871 nan 0.0500 -0.0279
## 100 2.4228 nan 0.0500 -0.0154
## 120 2.1971 nan 0.0500 -0.0238
## 140 1.9685 nan 0.0500 -0.0051
## 160 1.8083 nan 0.0500 -0.0032
## 180 1.6591 nan 0.0500 -0.0111
## 200 1.5165 nan 0.0500 -0.0174
## 220 1.3881 nan 0.0500 -0.0046
## 240 1.2850 nan 0.0500 -0.0086
## 260 1.1870 nan 0.0500 -0.0076
## 280 1.0969 nan 0.0500 -0.0124
## 300 1.0232 nan 0.0500 -0.0125
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 56.3257 nan 0.0500 4.9082
## 2 51.7223 nan 0.0500 4.1841
## 3 47.2697 nan 0.0500 4.0280
## 4 43.3561 nan 0.0500 3.7546
## 5 39.8132 nan 0.0500 3.4950
## 6 36.6496 nan 0.0500 3.0146
## 7 33.7619 nan 0.0500 2.8775
## 8 31.0063 nan 0.0500 2.6138
## 9 28.8234 nan 0.0500 2.1678
## 10 26.6081 nan 0.0500 2.1545
## 20 13.1800 nan 0.0500 0.7379
## 40 5.3308 nan 0.0500 0.1217
## 60 3.7864 nan 0.0500 -0.0058
## 80 3.2331 nan 0.0500 -0.0191
## 100 2.9085 nan 0.0500 -0.0242
## 120 2.6610 nan 0.0500 -0.0175
## 140 2.4777 nan 0.0500 -0.0077
## 160 2.3397 nan 0.0500 -0.0256
## 180 2.2029 nan 0.0500 -0.0218
## 200 2.0719 nan 0.0500 -0.0258
## 220 1.9302 nan 0.0500 -0.0066
## 240 1.8126 nan 0.0500 -0.0084
## 260 1.7167 nan 0.0500 -0.0163
## 280 1.6456 nan 0.0500 -0.0303
## 300 1.5770 nan 0.0500 -0.0154
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 56.6400 nan 0.0500 4.3550
## 2 52.0289 nan 0.0500 4.9811
## 3 47.9229 nan 0.0500 3.8790
## 4 44.0696 nan 0.0500 4.2387
## 5 40.5396 nan 0.0500 3.5277
## 6 37.4770 nan 0.0500 2.9603
## 7 34.4191 nan 0.0500 2.9682
## 8 31.6216 nan 0.0500 2.6704
## 9 29.2256 nan 0.0500 2.2461
## 10 27.0938 nan 0.0500 2.3321
## 20 13.3959 nan 0.0500 0.6797
## 40 5.6904 nan 0.0500 0.1482
## 60 4.1340 nan 0.0500 0.0103
## 80 3.6248 nan 0.0500 -0.0299
## 100 3.3372 nan 0.0500 -0.0193
## 120 3.0990 nan 0.0500 -0.0128
## 140 2.9222 nan 0.0500 -0.0148
## 160 2.7660 nan 0.0500 -0.0206
## 180 2.6155 nan 0.0500 -0.0071
## 200 2.4911 nan 0.0500 -0.0093
## 220 2.3656 nan 0.0500 -0.0193
## 240 2.2562 nan 0.0500 -0.0124
## 260 2.1599 nan 0.0500 -0.0316
## 280 2.0814 nan 0.0500 -0.0160
## 300 1.9923 nan 0.0500 -0.0011
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 54.2847 nan 0.1000 7.5454
## 2 48.6925 nan 0.1000 5.3311
## 3 44.1672 nan 0.1000 5.0422
## 4 39.6491 nan 0.1000 4.4760
## 5 35.7243 nan 0.1000 3.6096
## 6 32.6440 nan 0.1000 2.8981
## 7 29.4232 nan 0.1000 2.9324
## 8 27.0610 nan 0.1000 2.1609
## 9 24.9332 nan 0.1000 1.9953
## 10 22.8532 nan 0.1000 1.8774
## 20 11.9913 nan 0.1000 0.5764
## 40 5.9687 nan 0.1000 0.0648
## 60 4.6823 nan 0.1000 0.0043
## 80 4.3640 nan 0.1000 -0.0267
## 100 4.1831 nan 0.1000 -0.0480
## 120 4.0730 nan 0.1000 -0.0253
## 140 3.9445 nan 0.1000 -0.0369
## 160 3.8502 nan 0.1000 0.0005
## 180 3.7419 nan 0.1000 -0.0084
## 200 3.6585 nan 0.1000 -0.0078
## 220 3.5719 nan 0.1000 -0.0146
## 240 3.4860 nan 0.1000 -0.0248
## 260 3.4062 nan 0.1000 -0.0156
## 280 3.3333 nan 0.1000 -0.0101
## 300 3.2440 nan 0.1000 -0.0143
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 54.4360 nan 0.1000 7.1786
## 2 48.2436 nan 0.1000 5.9560
## 3 42.9664 nan 0.1000 4.7632
## 4 38.5017 nan 0.1000 3.8203
## 5 34.9034 nan 0.1000 3.6767
## 6 31.8816 nan 0.1000 3.1260
## 7 28.7641 nan 0.1000 2.6304
## 8 26.3670 nan 0.1000 2.1303
## 9 24.0570 nan 0.1000 1.9654
## 10 21.9052 nan 0.1000 1.5430
## 20 11.6009 nan 0.1000 0.4593
## 40 6.0548 nan 0.1000 0.0773
## 60 4.7745 nan 0.1000 -0.0061
## 80 4.4454 nan 0.1000 -0.0107
## 100 4.2585 nan 0.1000 -0.0184
## 120 4.1151 nan 0.1000 -0.0049
## 140 4.0180 nan 0.1000 -0.0325
## 160 3.9088 nan 0.1000 -0.0065
## 180 3.8272 nan 0.1000 -0.0172
## 200 3.7435 nan 0.1000 -0.0121
## 220 3.6725 nan 0.1000 -0.0367
## 240 3.5888 nan 0.1000 -0.0115
## 260 3.5253 nan 0.1000 -0.0218
## 280 3.4719 nan 0.1000 -0.0266
## 300 3.4208 nan 0.1000 -0.0139
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 53.9771 nan 0.1000 7.0895
## 2 47.9560 nan 0.1000 5.8764
## 3 43.0336 nan 0.1000 4.8825
## 4 38.8824 nan 0.1000 3.9261
## 5 35.1650 nan 0.1000 3.3880
## 6 31.7645 nan 0.1000 3.4813
## 7 28.9339 nan 0.1000 2.4061
## 8 26.3186 nan 0.1000 2.1991
## 9 24.0556 nan 0.1000 1.9092
## 10 22.2098 nan 0.1000 1.9348
## 20 12.0798 nan 0.1000 0.5723
## 40 6.2028 nan 0.1000 0.1175
## 60 5.0612 nan 0.1000 0.0113
## 80 4.7066 nan 0.1000 -0.0083
## 100 4.5007 nan 0.1000 -0.0166
## 120 4.3527 nan 0.1000 -0.0188
## 140 4.2346 nan 0.1000 -0.0182
## 160 4.1149 nan 0.1000 -0.0065
## 180 4.0134 nan 0.1000 -0.0158
## 200 3.9291 nan 0.1000 -0.0067
## 220 3.8515 nan 0.1000 -0.0139
## 240 3.7660 nan 0.1000 -0.0163
## 260 3.7190 nan 0.1000 -0.0038
## 280 3.6664 nan 0.1000 -0.0144
## 300 3.5887 nan 0.1000 -0.0125
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 51.7831 nan 0.1000 9.1923
## 2 43.9110 nan 0.1000 8.8358
## 3 37.2493 nan 0.1000 6.8920
## 4 31.9451 nan 0.1000 5.2992
## 5 28.0059 nan 0.1000 3.8268
## 6 24.2273 nan 0.1000 3.6710
## 7 21.1069 nan 0.1000 2.6253
## 8 18.8089 nan 0.1000 2.3356
## 9 16.6477 nan 0.1000 2.1193
## 10 15.1227 nan 0.1000 1.4087
## 20 6.4822 nan 0.1000 0.2781
## 40 3.6473 nan 0.1000 0.0004
## 60 3.0684 nan 0.1000 -0.0373
## 80 2.6830 nan 0.1000 -0.0257
## 100 2.3901 nan 0.1000 -0.0758
## 120 2.1295 nan 0.1000 -0.0589
## 140 1.9358 nan 0.1000 -0.0120
## 160 1.7685 nan 0.1000 -0.0249
## 180 1.5799 nan 0.1000 -0.0172
## 200 1.4666 nan 0.1000 -0.0247
## 220 1.3486 nan 0.1000 -0.0160
## 240 1.2328 nan 0.1000 -0.0135
## 260 1.1368 nan 0.1000 -0.0163
## 280 1.0465 nan 0.1000 -0.0074
## 300 0.9787 nan 0.1000 -0.0133
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 52.4299 nan 0.1000 9.3723
## 2 44.2958 nan 0.1000 8.8230
## 3 37.6021 nan 0.1000 6.3263
## 4 32.4734 nan 0.1000 5.4821
## 5 27.9151 nan 0.1000 4.2789
## 6 24.1962 nan 0.1000 3.4308
## 7 21.0915 nan 0.1000 3.0697
## 8 18.3793 nan 0.1000 2.0839
## 9 16.3224 nan 0.1000 1.7534
## 10 14.6533 nan 0.1000 1.7295
## 20 6.3795 nan 0.1000 0.2951
## 40 3.7911 nan 0.1000 -0.0214
## 60 3.2749 nan 0.1000 -0.0337
## 80 2.9521 nan 0.1000 -0.0505
## 100 2.6855 nan 0.1000 -0.0448
## 120 2.4726 nan 0.1000 -0.0195
## 140 2.3036 nan 0.1000 -0.0284
## 160 2.1852 nan 0.1000 -0.0343
## 180 2.0308 nan 0.1000 -0.0130
## 200 1.9137 nan 0.1000 -0.0482
## 220 1.7811 nan 0.1000 -0.0243
## 240 1.7000 nan 0.1000 -0.0366
## 260 1.6306 nan 0.1000 -0.0191
## 280 1.5485 nan 0.1000 -0.0163
## 300 1.4767 nan 0.1000 -0.0282
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 51.6957 nan 0.1000 10.2754
## 2 43.9888 nan 0.1000 5.8718
## 3 37.2893 nan 0.1000 6.3956
## 4 31.7999 nan 0.1000 5.0527
## 5 27.3631 nan 0.1000 4.6902
## 6 23.6505 nan 0.1000 3.0164
## 7 20.5370 nan 0.1000 2.7276
## 8 17.9467 nan 0.1000 2.4883
## 9 16.2444 nan 0.1000 1.7189
## 10 14.4171 nan 0.1000 1.7024
## 20 6.3703 nan 0.1000 0.2859
## 40 4.0622 nan 0.1000 -0.0069
## 60 3.4892 nan 0.1000 -0.0649
## 80 3.1642 nan 0.1000 -0.0277
## 100 2.9496 nan 0.1000 -0.0575
## 120 2.7454 nan 0.1000 -0.0347
## 140 2.6094 nan 0.1000 -0.0205
## 160 2.4530 nan 0.1000 -0.0320
## 180 2.3304 nan 0.1000 -0.0473
## 200 2.1898 nan 0.1000 -0.0050
## 220 2.0959 nan 0.1000 -0.0191
## 240 1.9861 nan 0.1000 -0.0119
## 260 1.8927 nan 0.1000 -0.0231
## 280 1.8116 nan 0.1000 -0.0159
## 300 1.7470 nan 0.1000 -0.0239
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 51.5094 nan 0.1000 10.0884
## 2 42.6263 nan 0.1000 7.9548
## 3 36.0184 nan 0.1000 7.0857
## 4 30.6992 nan 0.1000 5.2137
## 5 26.1656 nan 0.1000 4.0115
## 6 22.3809 nan 0.1000 3.1315
## 7 19.2653 nan 0.1000 2.8562
## 8 16.5477 nan 0.1000 2.4638
## 9 14.4104 nan 0.1000 1.9341
## 10 12.7724 nan 0.1000 1.4613
## 20 4.9430 nan 0.1000 0.3141
## 40 2.8922 nan 0.1000 -0.0826
## 60 2.2484 nan 0.1000 -0.0216
## 80 1.8591 nan 0.1000 -0.0362
## 100 1.5492 nan 0.1000 -0.0420
## 120 1.3282 nan 0.1000 -0.0318
## 140 1.1419 nan 0.1000 -0.0140
## 160 0.9678 nan 0.1000 -0.0162
## 180 0.8545 nan 0.1000 -0.0158
## 200 0.7652 nan 0.1000 -0.0185
## 220 0.6638 nan 0.1000 -0.0193
## 240 0.5770 nan 0.1000 -0.0133
## 260 0.5156 nan 0.1000 -0.0055
## 280 0.4601 nan 0.1000 -0.0049
## 300 0.4192 nan 0.1000 -0.0075
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 51.5295 nan 0.1000 10.7635
## 2 42.9585 nan 0.1000 8.2234
## 3 36.4052 nan 0.1000 6.6474
## 4 31.1299 nan 0.1000 4.5835
## 5 26.2568 nan 0.1000 4.3051
## 6 22.3920 nan 0.1000 3.5802
## 7 19.3733 nan 0.1000 2.9887
## 8 16.7005 nan 0.1000 2.3146
## 9 14.6994 nan 0.1000 1.9832
## 10 13.0350 nan 0.1000 1.5186
## 20 5.2289 nan 0.1000 0.1753
## 40 3.0827 nan 0.1000 -0.0080
## 60 2.6379 nan 0.1000 -0.0207
## 80 2.2997 nan 0.1000 -0.0350
## 100 2.0528 nan 0.1000 -0.0432
## 120 1.8291 nan 0.1000 -0.0384
## 140 1.6362 nan 0.1000 -0.0106
## 160 1.4719 nan 0.1000 -0.0286
## 180 1.3424 nan 0.1000 -0.0254
## 200 1.2191 nan 0.1000 -0.0260
## 220 1.1263 nan 0.1000 -0.0248
## 240 1.0218 nan 0.1000 -0.0117
## 260 0.9483 nan 0.1000 -0.0149
## 280 0.8664 nan 0.1000 -0.0131
## 300 0.8018 nan 0.1000 -0.0111
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 51.5351 nan 0.1000 9.6900
## 2 43.1175 nan 0.1000 8.7664
## 3 36.3583 nan 0.1000 6.5872
## 4 30.3095 nan 0.1000 5.8680
## 5 25.5715 nan 0.1000 4.2419
## 6 22.0715 nan 0.1000 3.6646
## 7 19.0115 nan 0.1000 3.2653
## 8 16.6682 nan 0.1000 2.4997
## 9 14.6179 nan 0.1000 1.9630
## 10 12.9433 nan 0.1000 1.5236
## 20 5.6848 nan 0.1000 0.2340
## 40 3.7360 nan 0.1000 -0.0338
## 60 3.2756 nan 0.1000 -0.0596
## 80 2.9915 nan 0.1000 -0.0369
## 100 2.7077 nan 0.1000 -0.0549
## 120 2.4969 nan 0.1000 -0.0376
## 140 2.3198 nan 0.1000 -0.0333
## 160 2.1127 nan 0.1000 -0.0418
## 180 1.9531 nan 0.1000 -0.0190
## 200 1.8175 nan 0.1000 -0.0179
## 220 1.6914 nan 0.1000 -0.0225
## 240 1.5730 nan 0.1000 -0.0135
## 260 1.4840 nan 0.1000 -0.0196
## 280 1.3939 nan 0.1000 -0.0142
## 300 1.3282 nan 0.1000 -0.0197
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 61.2782 nan 0.0100 0.7547
## 2 60.4911 nan 0.0100 0.7609
## 3 59.8464 nan 0.0100 0.6957
## 4 59.1555 nan 0.0100 0.7994
## 5 58.4687 nan 0.0100 0.7077
## 6 57.7765 nan 0.0100 0.6926
## 7 57.0277 nan 0.0100 0.6271
## 8 56.3670 nan 0.0100 0.6321
## 9 55.6581 nan 0.0100 0.6682
## 10 55.0170 nan 0.0100 0.6144
## 20 49.3185 nan 0.0100 0.5477
## 40 39.8234 nan 0.0100 0.3762
## 60 32.7438 nan 0.0100 0.3144
## 80 27.4280 nan 0.0100 0.2301
## 100 23.1311 nan 0.0100 0.1549
## 120 19.8965 nan 0.0100 0.1442
## 140 17.3439 nan 0.0100 0.0960
## 160 15.3253 nan 0.0100 0.0917
## 180 13.5949 nan 0.0100 0.0590
## 200 12.2595 nan 0.0100 0.0276
## 220 11.0968 nan 0.0100 0.0404
## 240 10.1079 nan 0.0100 0.0339
## 260 9.2429 nan 0.0100 0.0225
## 280 8.5532 nan 0.0100 0.0331
## 300 7.9692 nan 0.0100 0.0274
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 61.2032 nan 0.0100 0.7791
## 2 60.4498 nan 0.0100 0.7671
## 3 59.7171 nan 0.0100 0.6749
## 4 59.1124 nan 0.0100 0.6010
## 5 58.3623 nan 0.0100 0.6742
## 6 57.6567 nan 0.0100 0.6628
## 7 56.9899 nan 0.0100 0.7006
## 8 56.2986 nan 0.0100 0.6062
## 9 55.5947 nan 0.0100 0.6568
## 10 54.9510 nan 0.0100 0.6569
## 20 49.0352 nan 0.0100 0.5077
## 40 39.5332 nan 0.0100 0.4050
## 60 32.5387 nan 0.0100 0.3055
## 80 27.1126 nan 0.0100 0.1973
## 100 23.0153 nan 0.0100 0.1716
## 120 19.8695 nan 0.0100 0.1226
## 140 17.2766 nan 0.0100 0.1083
## 160 15.2465 nan 0.0100 0.0742
## 180 13.5743 nan 0.0100 0.0529
## 200 12.1343 nan 0.0100 0.0602
## 220 10.9952 nan 0.0100 0.0422
## 240 10.0011 nan 0.0100 0.0347
## 260 9.1952 nan 0.0100 0.0314
## 280 8.4795 nan 0.0100 0.0161
## 300 7.8864 nan 0.0100 0.0070
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 61.1751 nan 0.0100 0.7565
## 2 60.4646 nan 0.0100 0.7551
## 3 59.7129 nan 0.0100 0.7414
## 4 58.9753 nan 0.0100 0.6788
## 5 58.2159 nan 0.0100 0.6441
## 6 57.4814 nan 0.0100 0.6932
## 7 56.7929 nan 0.0100 0.6312
## 8 56.0925 nan 0.0100 0.6759
## 9 55.4329 nan 0.0100 0.6614
## 10 54.7291 nan 0.0100 0.6077
## 20 48.7355 nan 0.0100 0.5156
## 40 39.4375 nan 0.0100 0.3859
## 60 32.4370 nan 0.0100 0.2478
## 80 27.0441 nan 0.0100 0.2366
## 100 22.8406 nan 0.0100 0.1715
## 120 19.6887 nan 0.0100 0.1411
## 140 17.1510 nan 0.0100 0.0925
## 160 15.1039 nan 0.0100 0.0590
## 180 13.4811 nan 0.0100 0.0641
## 200 12.1224 nan 0.0100 0.0511
## 220 10.9726 nan 0.0100 0.0379
## 240 10.0197 nan 0.0100 0.0383
## 260 9.2190 nan 0.0100 0.0167
## 280 8.5204 nan 0.0100 0.0263
## 300 7.9473 nan 0.0100 0.0253
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 60.9673 nan 0.0100 1.1014
## 2 60.0113 nan 0.0100 1.0332
## 3 59.0835 nan 0.0100 0.9142
## 4 58.1361 nan 0.0100 0.9753
## 5 57.2506 nan 0.0100 0.9082
## 6 56.2395 nan 0.0100 0.8998
## 7 55.2943 nan 0.0100 0.8753
## 8 54.3674 nan 0.0100 0.8091
## 9 53.4745 nan 0.0100 0.8212
## 10 52.6147 nan 0.0100 0.8496
## 20 44.9554 nan 0.0100 0.7143
## 40 33.1898 nan 0.0100 0.4872
## 60 25.0167 nan 0.0100 0.3252
## 80 19.1779 nan 0.0100 0.2385
## 100 15.0999 nan 0.0100 0.1559
## 120 12.2186 nan 0.0100 0.1355
## 140 10.0133 nan 0.0100 0.0732
## 160 8.4532 nan 0.0100 0.0629
## 180 7.2764 nan 0.0100 0.0459
## 200 6.4265 nan 0.0100 0.0390
## 220 5.7298 nan 0.0100 0.0152
## 240 5.1990 nan 0.0100 0.0128
## 260 4.8165 nan 0.0100 0.0051
## 280 4.5014 nan 0.0100 0.0048
## 300 4.2759 nan 0.0100 0.0027
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 60.9618 nan 0.0100 0.9912
## 2 59.9884 nan 0.0100 0.9463
## 3 58.9741 nan 0.0100 1.0465
## 4 57.9778 nan 0.0100 0.9638
## 5 57.0541 nan 0.0100 0.9541
## 6 56.1177 nan 0.0100 0.9155
## 7 55.1801 nan 0.0100 0.9345
## 8 54.3021 nan 0.0100 0.8906
## 9 53.4836 nan 0.0100 0.8987
## 10 52.6431 nan 0.0100 0.8870
## 20 44.8019 nan 0.0100 0.7496
## 40 33.1322 nan 0.0100 0.4327
## 60 24.8941 nan 0.0100 0.3239
## 80 19.2709 nan 0.0100 0.2609
## 100 15.1453 nan 0.0100 0.1614
## 120 12.2285 nan 0.0100 0.0973
## 140 10.1777 nan 0.0100 0.0776
## 160 8.5915 nan 0.0100 0.0510
## 180 7.4301 nan 0.0100 0.0347
## 200 6.5481 nan 0.0100 0.0367
## 220 5.8612 nan 0.0100 0.0246
## 240 5.3379 nan 0.0100 0.0139
## 260 4.9536 nan 0.0100 0.0146
## 280 4.6432 nan 0.0100 0.0056
## 300 4.4226 nan 0.0100 0.0011
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 60.9484 nan 0.0100 0.9642
## 2 59.9405 nan 0.0100 1.1000
## 3 58.9755 nan 0.0100 0.9454
## 4 58.0619 nan 0.0100 0.9695
## 5 57.1092 nan 0.0100 0.9359
## 6 56.1662 nan 0.0100 0.7976
## 7 55.2474 nan 0.0100 0.8715
## 8 54.3634 nan 0.0100 0.7532
## 9 53.5102 nan 0.0100 0.9008
## 10 52.6693 nan 0.0100 0.8708
## 20 44.8179 nan 0.0100 0.6580
## 40 33.1220 nan 0.0100 0.4176
## 60 24.9934 nan 0.0100 0.3294
## 80 19.2953 nan 0.0100 0.2322
## 100 15.2673 nan 0.0100 0.1688
## 120 12.3992 nan 0.0100 0.1088
## 140 10.2690 nan 0.0100 0.0764
## 160 8.7538 nan 0.0100 0.0511
## 180 7.5496 nan 0.0100 0.0419
## 200 6.6881 nan 0.0100 0.0216
## 220 6.0339 nan 0.0100 0.0204
## 240 5.5379 nan 0.0100 0.0164
## 260 5.1856 nan 0.0100 0.0136
## 280 4.8905 nan 0.0100 0.0114
## 300 4.6574 nan 0.0100 0.0027
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 60.9066 nan 0.0100 1.0793
## 2 59.8637 nan 0.0100 1.1211
## 3 58.7984 nan 0.0100 1.0466
## 4 57.7997 nan 0.0100 0.9490
## 5 56.8108 nan 0.0100 0.8728
## 6 55.8808 nan 0.0100 0.9942
## 7 54.9091 nan 0.0100 0.8586
## 8 53.9576 nan 0.0100 0.9741
## 9 52.9840 nan 0.0100 0.9174
## 10 52.0550 nan 0.0100 0.9862
## 20 43.9723 nan 0.0100 0.7460
## 40 31.6908 nan 0.0100 0.4533
## 60 23.2891 nan 0.0100 0.3550
## 80 17.4293 nan 0.0100 0.2488
## 100 13.2933 nan 0.0100 0.1942
## 120 10.4793 nan 0.0100 0.1200
## 140 8.5063 nan 0.0100 0.0668
## 160 7.0616 nan 0.0100 0.0342
## 180 6.0231 nan 0.0100 0.0283
## 200 5.2383 nan 0.0100 0.0169
## 220 4.6363 nan 0.0100 0.0148
## 240 4.2156 nan 0.0100 0.0078
## 260 3.8933 nan 0.0100 0.0077
## 280 3.6321 nan 0.0100 0.0050
## 300 3.4228 nan 0.0100 0.0033
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 60.8542 nan 0.0100 1.0554
## 2 59.7785 nan 0.0100 0.9839
## 3 58.7497 nan 0.0100 0.9876
## 4 57.7135 nan 0.0100 1.1079
## 5 56.6976 nan 0.0100 0.9991
## 6 55.6967 nan 0.0100 0.9325
## 7 54.7520 nan 0.0100 0.9363
## 8 53.8409 nan 0.0100 0.9579
## 9 52.9597 nan 0.0100 0.9174
## 10 52.0546 nan 0.0100 0.8634
## 20 44.0498 nan 0.0100 0.7668
## 40 31.6326 nan 0.0100 0.4931
## 60 23.2712 nan 0.0100 0.3170
## 80 17.4886 nan 0.0100 0.2272
## 100 13.4487 nan 0.0100 0.1517
## 120 10.5255 nan 0.0100 0.1185
## 140 8.5247 nan 0.0100 0.0850
## 160 7.0966 nan 0.0100 0.0504
## 180 6.0861 nan 0.0100 0.0232
## 200 5.3776 nan 0.0100 0.0210
## 220 4.8300 nan 0.0100 0.0014
## 240 4.4625 nan 0.0100 0.0132
## 260 4.1338 nan 0.0100 0.0102
## 280 3.9148 nan 0.0100 0.0011
## 300 3.7296 nan 0.0100 0.0021
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 60.8955 nan 0.0100 1.0620
## 2 59.8036 nan 0.0100 0.9926
## 3 58.8117 nan 0.0100 1.1072
## 4 57.8520 nan 0.0100 0.9819
## 5 56.8469 nan 0.0100 0.8601
## 6 55.8695 nan 0.0100 1.0273
## 7 54.9189 nan 0.0100 0.8766
## 8 54.0025 nan 0.0100 0.9191
## 9 53.1187 nan 0.0100 0.8876
## 10 52.1809 nan 0.0100 0.9734
## 20 44.2258 nan 0.0100 0.7035
## 40 31.9399 nan 0.0100 0.4012
## 60 23.5770 nan 0.0100 0.3014
## 80 17.6580 nan 0.0100 0.2422
## 100 13.6456 nan 0.0100 0.1330
## 120 10.8694 nan 0.0100 0.1122
## 140 8.8884 nan 0.0100 0.0833
## 160 7.5281 nan 0.0100 0.0528
## 180 6.5305 nan 0.0100 0.0355
## 200 5.8123 nan 0.0100 0.0167
## 220 5.2650 nan 0.0100 0.0144
## 240 4.8759 nan 0.0100 0.0103
## 260 4.5771 nan 0.0100 0.0071
## 280 4.3504 nan 0.0100 0.0051
## 300 4.1737 nan 0.0100 0.0022
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 58.2006 nan 0.0500 3.7901
## 2 55.2052 nan 0.0500 3.2358
## 3 51.9308 nan 0.0500 3.1021
## 4 48.9960 nan 0.0500 2.8302
## 5 46.3911 nan 0.0500 2.6234
## 6 43.8995 nan 0.0500 2.2379
## 7 41.5016 nan 0.0500 2.1831
## 8 39.6352 nan 0.0500 1.9675
## 9 37.7607 nan 0.0500 2.0399
## 10 36.0613 nan 0.0500 1.7732
## 20 23.1878 nan 0.0500 0.9456
## 40 11.8278 nan 0.0500 0.2510
## 60 7.7737 nan 0.0500 0.1266
## 80 5.9591 nan 0.0500 0.0387
## 100 5.0405 nan 0.0500 0.0181
## 120 4.5846 nan 0.0500 -0.0046
## 140 4.3993 nan 0.0500 -0.0107
## 160 4.2509 nan 0.0500 -0.0193
## 180 4.1496 nan 0.0500 0.0017
## 200 4.0758 nan 0.0500 -0.0348
## 220 3.9887 nan 0.0500 -0.0008
## 240 3.9232 nan 0.0500 -0.0018
## 260 3.8710 nan 0.0500 -0.0108
## 280 3.8077 nan 0.0500 -0.0006
## 300 3.7601 nan 0.0500 -0.0061
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 58.3583 nan 0.0500 3.7702
## 2 54.6627 nan 0.0500 3.3922
## 3 51.5871 nan 0.0500 2.9825
## 4 48.6161 nan 0.0500 2.8540
## 5 45.7592 nan 0.0500 2.4412
## 6 43.2429 nan 0.0500 2.3682
## 7 40.8529 nan 0.0500 2.1225
## 8 38.8422 nan 0.0500 1.9111
## 9 36.8274 nan 0.0500 1.6896
## 10 35.1189 nan 0.0500 1.6276
## 20 22.6289 nan 0.0500 0.8623
## 40 12.0454 nan 0.0500 0.3335
## 60 8.0545 nan 0.0500 0.0532
## 80 6.0855 nan 0.0500 0.0602
## 100 5.1732 nan 0.0500 0.0125
## 120 4.7647 nan 0.0500 0.0066
## 140 4.5707 nan 0.0500 -0.0092
## 160 4.4135 nan 0.0500 -0.0063
## 180 4.2780 nan 0.0500 -0.0085
## 200 4.1921 nan 0.0500 -0.0186
## 220 4.1194 nan 0.0500 0.0002
## 240 4.0499 nan 0.0500 -0.0172
## 260 4.0078 nan 0.0500 -0.0100
## 280 3.9571 nan 0.0500 -0.0178
## 300 3.9042 nan 0.0500 -0.0222
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 58.1475 nan 0.0500 3.4288
## 2 54.9305 nan 0.0500 3.3203
## 3 51.6950 nan 0.0500 3.1132
## 4 48.5420 nan 0.0500 2.7732
## 5 45.8491 nan 0.0500 2.4686
## 6 43.7292 nan 0.0500 1.9523
## 7 41.5442 nan 0.0500 2.3096
## 8 39.2984 nan 0.0500 2.0395
## 9 37.3538 nan 0.0500 1.8446
## 10 35.5245 nan 0.0500 1.6948
## 20 22.5341 nan 0.0500 0.8060
## 40 11.8316 nan 0.0500 0.2360
## 60 7.7154 nan 0.0500 0.1049
## 80 6.0048 nan 0.0500 0.0531
## 100 5.2107 nan 0.0500 -0.0017
## 120 4.8325 nan 0.0500 -0.0099
## 140 4.6091 nan 0.0500 -0.0144
## 160 4.4715 nan 0.0500 -0.0115
## 180 4.3627 nan 0.0500 -0.0154
## 200 4.2785 nan 0.0500 -0.0046
## 220 4.1841 nan 0.0500 -0.0057
## 240 4.1275 nan 0.0500 -0.0297
## 260 4.0548 nan 0.0500 -0.0211
## 280 3.9984 nan 0.0500 -0.0098
## 300 3.9480 nan 0.0500 -0.0178
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 56.8468 nan 0.0500 4.9823
## 2 52.2897 nan 0.0500 4.2509
## 3 48.2493 nan 0.0500 3.8500
## 4 44.2044 nan 0.0500 3.9502
## 5 40.9998 nan 0.0500 3.1167
## 6 37.9338 nan 0.0500 3.5273
## 7 35.0813 nan 0.0500 2.7784
## 8 32.5656 nan 0.0500 2.6083
## 9 30.2512 nan 0.0500 2.3750
## 10 28.1495 nan 0.0500 1.8037
## 20 14.6402 nan 0.0500 0.7339
## 40 6.2141 nan 0.0500 0.2007
## 60 4.1856 nan 0.0500 0.0297
## 80 3.5389 nan 0.0500 -0.0063
## 100 3.2361 nan 0.0500 -0.0130
## 120 2.9660 nan 0.0500 -0.0140
## 140 2.7125 nan 0.0500 -0.0170
## 160 2.5298 nan 0.0500 -0.0096
## 180 2.3619 nan 0.0500 -0.0103
## 200 2.2383 nan 0.0500 -0.0148
## 220 2.1349 nan 0.0500 -0.0065
## 240 2.0271 nan 0.0500 -0.0095
## 260 1.9195 nan 0.0500 -0.0094
## 280 1.8284 nan 0.0500 -0.0074
## 300 1.7672 nan 0.0500 -0.0156
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 56.9699 nan 0.0500 4.8253
## 2 52.2343 nan 0.0500 4.3562
## 3 48.2032 nan 0.0500 3.6253
## 4 44.6032 nan 0.0500 3.7686
## 5 41.6168 nan 0.0500 3.4270
## 6 38.4945 nan 0.0500 3.2866
## 7 35.6761 nan 0.0500 2.6693
## 8 33.0318 nan 0.0500 2.6381
## 9 30.6494 nan 0.0500 2.4535
## 10 28.4127 nan 0.0500 2.2697
## 20 14.8107 nan 0.0500 0.8712
## 40 6.3471 nan 0.0500 0.1360
## 60 4.3693 nan 0.0500 0.0035
## 80 3.7617 nan 0.0500 0.0149
## 100 3.4241 nan 0.0500 -0.0335
## 120 3.2004 nan 0.0500 -0.0164
## 140 3.0228 nan 0.0500 -0.0222
## 160 2.8736 nan 0.0500 -0.0215
## 180 2.7380 nan 0.0500 -0.0256
## 200 2.6255 nan 0.0500 -0.0063
## 220 2.5174 nan 0.0500 -0.0053
## 240 2.4024 nan 0.0500 -0.0222
## 260 2.3129 nan 0.0500 -0.0090
## 280 2.2234 nan 0.0500 -0.0149
## 300 2.1569 nan 0.0500 -0.0081
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 57.0318 nan 0.0500 5.2241
## 2 52.3588 nan 0.0500 4.3338
## 3 48.2314 nan 0.0500 3.8491
## 4 44.6297 nan 0.0500 3.8542
## 5 41.2513 nan 0.0500 3.3895
## 6 37.9487 nan 0.0500 2.9241
## 7 35.1918 nan 0.0500 2.8625
## 8 32.4621 nan 0.0500 2.2926
## 9 30.1004 nan 0.0500 2.1692
## 10 28.0571 nan 0.0500 2.1696
## 20 14.7817 nan 0.0500 0.8538
## 40 6.6302 nan 0.0500 0.1822
## 60 4.6501 nan 0.0500 0.0040
## 80 4.0142 nan 0.0500 0.0014
## 100 3.7017 nan 0.0500 -0.0054
## 120 3.4814 nan 0.0500 -0.0426
## 140 3.2993 nan 0.0500 -0.0059
## 160 3.1524 nan 0.0500 -0.0381
## 180 3.0405 nan 0.0500 -0.0204
## 200 2.9298 nan 0.0500 -0.0072
## 220 2.8203 nan 0.0500 -0.0190
## 240 2.7347 nan 0.0500 -0.0033
## 260 2.6470 nan 0.0500 -0.0074
## 280 2.5829 nan 0.0500 -0.0202
## 300 2.4910 nan 0.0500 -0.0143
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 56.4199 nan 0.0500 6.1120
## 2 51.5760 nan 0.0500 4.7250
## 3 47.2701 nan 0.0500 4.0343
## 4 43.4141 nan 0.0500 3.8000
## 5 39.8707 nan 0.0500 3.5911
## 6 36.6415 nan 0.0500 3.0290
## 7 33.9256 nan 0.0500 2.8129
## 8 31.2809 nan 0.0500 2.3267
## 9 28.8015 nan 0.0500 2.0608
## 10 26.4720 nan 0.0500 1.8536
## 20 12.7148 nan 0.0500 0.8396
## 40 5.1527 nan 0.0500 0.1045
## 60 3.5056 nan 0.0500 0.0233
## 80 2.9058 nan 0.0500 0.0064
## 100 2.5195 nan 0.0500 -0.0095
## 120 2.2780 nan 0.0500 -0.0139
## 140 2.0464 nan 0.0500 -0.0225
## 160 1.8618 nan 0.0500 -0.0086
## 180 1.6955 nan 0.0500 -0.0071
## 200 1.5669 nan 0.0500 -0.0114
## 220 1.4465 nan 0.0500 -0.0047
## 240 1.3395 nan 0.0500 -0.0188
## 260 1.2408 nan 0.0500 -0.0101
## 280 1.1445 nan 0.0500 -0.0082
## 300 1.0658 nan 0.0500 -0.0069
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 56.7837 nan 0.0500 4.9749
## 2 51.9329 nan 0.0500 4.7119
## 3 47.7165 nan 0.0500 4.2591
## 4 43.7532 nan 0.0500 3.4393
## 5 40.3199 nan 0.0500 3.7191
## 6 37.1990 nan 0.0500 3.0032
## 7 34.1873 nan 0.0500 2.4679
## 8 31.5847 nan 0.0500 2.8507
## 9 29.1043 nan 0.0500 2.2560
## 10 26.8693 nan 0.0500 1.9285
## 20 13.4486 nan 0.0500 0.7077
## 40 5.4059 nan 0.0500 0.1273
## 60 3.7388 nan 0.0500 0.0116
## 80 3.1674 nan 0.0500 -0.0052
## 100 2.8128 nan 0.0500 -0.0083
## 120 2.5731 nan 0.0500 -0.0296
## 140 2.3753 nan 0.0500 -0.0186
## 160 2.2237 nan 0.0500 -0.0123
## 180 2.0932 nan 0.0500 -0.0134
## 200 1.9533 nan 0.0500 -0.0101
## 220 1.8481 nan 0.0500 -0.0152
## 240 1.7331 nan 0.0500 -0.0098
## 260 1.6449 nan 0.0500 -0.0103
## 280 1.5608 nan 0.0500 -0.0136
## 300 1.4904 nan 0.0500 -0.0185
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 56.8951 nan 0.0500 4.6450
## 2 52.0892 nan 0.0500 4.7157
## 3 47.7050 nan 0.0500 4.1144
## 4 43.7895 nan 0.0500 3.8474
## 5 40.3450 nan 0.0500 3.4873
## 6 37.1714 nan 0.0500 2.8507
## 7 34.1761 nan 0.0500 2.7566
## 8 31.4909 nan 0.0500 2.6038
## 9 29.0280 nan 0.0500 2.3011
## 10 26.8480 nan 0.0500 1.7609
## 20 12.9348 nan 0.0500 0.8350
## 40 5.5096 nan 0.0500 0.1303
## 60 4.0999 nan 0.0500 -0.0008
## 80 3.6296 nan 0.0500 -0.0071
## 100 3.3475 nan 0.0500 -0.0137
## 120 3.1169 nan 0.0500 -0.0224
## 140 2.9306 nan 0.0500 -0.0156
## 160 2.7564 nan 0.0500 -0.0088
## 180 2.6169 nan 0.0500 -0.0187
## 200 2.4813 nan 0.0500 -0.0045
## 220 2.3481 nan 0.0500 -0.0080
## 240 2.2431 nan 0.0500 -0.0033
## 260 2.1423 nan 0.0500 -0.0151
## 280 2.0568 nan 0.0500 -0.0260
## 300 1.9780 nan 0.0500 -0.0131
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 54.4942 nan 0.1000 7.4367
## 2 48.1030 nan 0.1000 6.0468
## 3 43.1471 nan 0.1000 4.8749
## 4 39.1182 nan 0.1000 4.1506
## 5 35.2624 nan 0.1000 3.8812
## 6 32.0207 nan 0.1000 3.1352
## 7 29.1942 nan 0.1000 2.7367
## 8 26.4993 nan 0.1000 2.3297
## 9 24.4903 nan 0.1000 1.9794
## 10 22.6163 nan 0.1000 1.4241
## 20 11.6790 nan 0.1000 0.4447
## 40 5.9052 nan 0.1000 0.1023
## 60 4.7377 nan 0.1000 0.0111
## 80 4.3624 nan 0.1000 -0.0610
## 100 4.1634 nan 0.1000 0.0030
## 120 3.9861 nan 0.1000 -0.0143
## 140 3.8581 nan 0.1000 0.0050
## 160 3.7432 nan 0.1000 -0.0134
## 180 3.6361 nan 0.1000 0.0018
## 200 3.5595 nan 0.1000 -0.0672
## 220 3.4629 nan 0.1000 -0.0114
## 240 3.3850 nan 0.1000 -0.0203
## 260 3.3223 nan 0.1000 -0.0070
## 280 3.2471 nan 0.1000 -0.0141
## 300 3.1868 nan 0.1000 -0.0422
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 54.3703 nan 0.1000 7.5508
## 2 48.1661 nan 0.1000 5.7058
## 3 42.9969 nan 0.1000 5.6435
## 4 38.7920 nan 0.1000 3.8153
## 5 35.0737 nan 0.1000 3.4045
## 6 31.5012 nan 0.1000 3.1884
## 7 28.3632 nan 0.1000 2.8261
## 8 25.9607 nan 0.1000 2.2638
## 9 23.9589 nan 0.1000 2.2215
## 10 22.1517 nan 0.1000 1.7520
## 20 11.4952 nan 0.1000 0.5707
## 40 6.0919 nan 0.1000 0.0186
## 60 4.8302 nan 0.1000 0.0059
## 80 4.5014 nan 0.1000 -0.0423
## 100 4.2902 nan 0.1000 -0.0120
## 120 4.1085 nan 0.1000 -0.0136
## 140 3.9673 nan 0.1000 -0.0211
## 160 3.8808 nan 0.1000 -0.0444
## 180 3.7951 nan 0.1000 -0.0263
## 200 3.6965 nan 0.1000 -0.0063
## 220 3.6136 nan 0.1000 -0.0085
## 240 3.5486 nan 0.1000 -0.0316
## 260 3.4780 nan 0.1000 -0.0060
## 280 3.4113 nan 0.1000 -0.0132
## 300 3.3488 nan 0.1000 -0.0232
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 54.5642 nan 0.1000 7.3150
## 2 48.3378 nan 0.1000 6.4479
## 3 42.7220 nan 0.1000 5.3661
## 4 38.3344 nan 0.1000 4.1302
## 5 34.7347 nan 0.1000 3.7821
## 6 31.4075 nan 0.1000 3.1892
## 7 28.9008 nan 0.1000 2.4299
## 8 26.3632 nan 0.1000 1.9049
## 9 24.3865 nan 0.1000 2.0272
## 10 22.5833 nan 0.1000 1.7961
## 20 11.9961 nan 0.1000 0.5546
## 40 6.0683 nan 0.1000 0.0527
## 60 4.8348 nan 0.1000 -0.0322
## 80 4.5493 nan 0.1000 -0.0050
## 100 4.3309 nan 0.1000 -0.0095
## 120 4.1797 nan 0.1000 -0.0638
## 140 4.0479 nan 0.1000 -0.0234
## 160 3.9653 nan 0.1000 -0.0235
## 180 3.8935 nan 0.1000 -0.0112
## 200 3.8346 nan 0.1000 -0.0406
## 220 3.7815 nan 0.1000 -0.0161
## 240 3.7158 nan 0.1000 -0.0452
## 260 3.6649 nan 0.1000 -0.0427
## 280 3.6115 nan 0.1000 -0.0052
## 300 3.5601 nan 0.1000 -0.0305
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 52.0842 nan 0.1000 9.3246
## 2 44.1789 nan 0.1000 7.8135
## 3 38.0786 nan 0.1000 5.3407
## 4 32.9572 nan 0.1000 5.6217
## 5 28.3216 nan 0.1000 4.1822
## 6 24.5225 nan 0.1000 3.5355
## 7 21.6613 nan 0.1000 3.0743
## 8 18.9192 nan 0.1000 2.6779
## 9 16.9583 nan 0.1000 2.0803
## 10 15.0642 nan 0.1000 1.8737
## 20 6.4147 nan 0.1000 0.3294
## 40 3.7682 nan 0.1000 -0.0453
## 60 3.1294 nan 0.1000 -0.0218
## 80 2.6326 nan 0.1000 -0.0061
## 100 2.3285 nan 0.1000 -0.0259
## 120 2.0895 nan 0.1000 -0.0145
## 140 1.8711 nan 0.1000 -0.0135
## 160 1.6898 nan 0.1000 -0.0104
## 180 1.5611 nan 0.1000 -0.0145
## 200 1.4394 nan 0.1000 -0.0098
## 220 1.3228 nan 0.1000 -0.0275
## 240 1.2025 nan 0.1000 -0.0196
## 260 1.1222 nan 0.1000 -0.0112
## 280 1.0373 nan 0.1000 -0.0068
## 300 0.9568 nan 0.1000 -0.0068
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 52.2851 nan 0.1000 9.4219
## 2 43.8049 nan 0.1000 6.9725
## 3 37.1925 nan 0.1000 6.5238
## 4 32.1329 nan 0.1000 4.1976
## 5 27.7888 nan 0.1000 3.9423
## 6 24.1978 nan 0.1000 2.5259
## 7 21.2532 nan 0.1000 2.8739
## 8 18.6324 nan 0.1000 2.4699
## 9 16.3798 nan 0.1000 1.4447
## 10 14.3329 nan 0.1000 1.8704
## 20 6.4610 nan 0.1000 0.2738
## 40 3.7799 nan 0.1000 -0.0184
## 60 3.1996 nan 0.1000 -0.0145
## 80 2.8827 nan 0.1000 -0.0418
## 100 2.6678 nan 0.1000 -0.0124
## 120 2.4172 nan 0.1000 -0.0390
## 140 2.2186 nan 0.1000 -0.0341
## 160 2.0577 nan 0.1000 -0.0232
## 180 1.9114 nan 0.1000 -0.0251
## 200 1.7889 nan 0.1000 -0.0183
## 220 1.6888 nan 0.1000 -0.0361
## 240 1.5788 nan 0.1000 -0.0148
## 260 1.5009 nan 0.1000 -0.0247
## 280 1.4380 nan 0.1000 -0.0233
## 300 1.3397 nan 0.1000 -0.0095
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 52.2246 nan 0.1000 10.4952
## 2 44.9604 nan 0.1000 7.0779
## 3 38.0291 nan 0.1000 6.3631
## 4 33.0004 nan 0.1000 5.4854
## 5 28.2929 nan 0.1000 3.8314
## 6 24.5448 nan 0.1000 3.5218
## 7 21.5809 nan 0.1000 3.0567
## 8 19.0817 nan 0.1000 2.0424
## 9 16.8944 nan 0.1000 2.1184
## 10 15.1068 nan 0.1000 1.7025
## 20 6.5239 nan 0.1000 0.2687
## 40 4.0757 nan 0.1000 -0.0082
## 60 3.6032 nan 0.1000 -0.0299
## 80 3.2191 nan 0.1000 -0.0143
## 100 2.9313 nan 0.1000 -0.0232
## 120 2.7037 nan 0.1000 -0.0126
## 140 2.5192 nan 0.1000 -0.0098
## 160 2.3595 nan 0.1000 -0.0099
## 180 2.2522 nan 0.1000 -0.0288
## 200 2.1502 nan 0.1000 -0.0191
## 220 2.0633 nan 0.1000 -0.0290
## 240 1.9872 nan 0.1000 -0.0178
## 260 1.8956 nan 0.1000 -0.0396
## 280 1.8139 nan 0.1000 -0.0134
## 300 1.7371 nan 0.1000 -0.0157
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 51.4508 nan 0.1000 9.6105
## 2 42.7810 nan 0.1000 7.7365
## 3 35.9116 nan 0.1000 7.0000
## 4 30.1271 nan 0.1000 5.4712
## 5 25.6356 nan 0.1000 4.3297
## 6 21.7710 nan 0.1000 3.0801
## 7 18.9673 nan 0.1000 3.0326
## 8 16.1625 nan 0.1000 2.6169
## 9 14.1362 nan 0.1000 1.8250
## 10 12.5215 nan 0.1000 1.3520
## 20 5.0271 nan 0.1000 0.2604
## 40 2.8100 nan 0.1000 -0.0053
## 60 2.1302 nan 0.1000 -0.0253
## 80 1.8126 nan 0.1000 -0.0342
## 100 1.5325 nan 0.1000 -0.0089
## 120 1.3051 nan 0.1000 -0.0288
## 140 1.0968 nan 0.1000 -0.0055
## 160 0.9408 nan 0.1000 -0.0174
## 180 0.8326 nan 0.1000 -0.0207
## 200 0.7318 nan 0.1000 -0.0101
## 220 0.6465 nan 0.1000 -0.0093
## 240 0.5778 nan 0.1000 -0.0209
## 260 0.5145 nan 0.1000 -0.0096
## 280 0.4642 nan 0.1000 -0.0078
## 300 0.4211 nan 0.1000 -0.0088
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 51.8478 nan 0.1000 9.4693
## 2 43.7489 nan 0.1000 8.0990
## 3 36.4801 nan 0.1000 5.8398
## 4 30.9473 nan 0.1000 5.3816
## 5 26.2613 nan 0.1000 4.2821
## 6 22.4795 nan 0.1000 3.1843
## 7 19.3738 nan 0.1000 2.8526
## 8 16.5866 nan 0.1000 2.2636
## 9 14.5540 nan 0.1000 1.8696
## 10 12.7029 nan 0.1000 1.6632
## 20 5.2177 nan 0.1000 0.1820
## 40 3.1737 nan 0.1000 -0.0396
## 60 2.6599 nan 0.1000 0.0013
## 80 2.3341 nan 0.1000 -0.0461
## 100 2.0495 nan 0.1000 -0.0389
## 120 1.8359 nan 0.1000 -0.0479
## 140 1.6264 nan 0.1000 -0.0224
## 160 1.4851 nan 0.1000 -0.0165
## 180 1.3316 nan 0.1000 -0.0085
## 200 1.2032 nan 0.1000 -0.0142
## 220 1.1055 nan 0.1000 -0.0185
## 240 1.0264 nan 0.1000 -0.0125
## 260 0.9342 nan 0.1000 -0.0153
## 280 0.8672 nan 0.1000 -0.0285
## 300 0.8035 nan 0.1000 -0.0138
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 50.9098 nan 0.1000 11.5385
## 2 42.7999 nan 0.1000 8.4313
## 3 36.1975 nan 0.1000 6.2216
## 4 30.6846 nan 0.1000 5.3458
## 5 26.2712 nan 0.1000 4.3463
## 6 22.6375 nan 0.1000 3.3744
## 7 19.4777 nan 0.1000 2.9858
## 8 16.8529 nan 0.1000 2.6947
## 9 14.7269 nan 0.1000 2.2670
## 10 12.8792 nan 0.1000 1.8642
## 20 5.5631 nan 0.1000 0.2767
## 40 3.6375 nan 0.1000 -0.0077
## 60 3.0863 nan 0.1000 -0.0401
## 80 2.7156 nan 0.1000 -0.0403
## 100 2.4505 nan 0.1000 -0.0594
## 120 2.2095 nan 0.1000 -0.0396
## 140 2.0110 nan 0.1000 -0.0229
## 160 1.8691 nan 0.1000 -0.0299
## 180 1.7029 nan 0.1000 -0.0270
## 200 1.5728 nan 0.1000 -0.0415
## 220 1.4702 nan 0.1000 -0.0198
## 240 1.3920 nan 0.1000 -0.0149
## 260 1.2846 nan 0.1000 -0.0267
## 280 1.2200 nan 0.1000 -0.0155
## 300 1.1637 nan 0.1000 -0.0109
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 59.6823 nan 0.0100 0.7368
## 2 58.9940 nan 0.0100 0.7574
## 3 58.2190 nan 0.0100 0.7148
## 4 57.5109 nan 0.0100 0.7034
## 5 56.7842 nan 0.0100 0.7185
## 6 56.0852 nan 0.0100 0.6905
## 7 55.4319 nan 0.0100 0.6603
## 8 54.7807 nan 0.0100 0.6777
## 9 54.0980 nan 0.0100 0.6482
## 10 53.4720 nan 0.0100 0.6282
## 20 47.4933 nan 0.0100 0.5268
## 40 38.3015 nan 0.0100 0.3697
## 60 31.4362 nan 0.0100 0.2878
## 80 26.1281 nan 0.0100 0.2077
## 100 22.0564 nan 0.0100 0.1724
## 120 18.9299 nan 0.0100 0.1284
## 140 16.4093 nan 0.0100 0.0435
## 160 14.3666 nan 0.0100 0.0787
## 180 12.7596 nan 0.0100 0.0684
## 200 11.4469 nan 0.0100 0.0551
## 220 10.3126 nan 0.0100 0.0414
## 240 9.4292 nan 0.0100 0.0337
## 260 8.6507 nan 0.0100 0.0331
## 280 8.0116 nan 0.0100 0.0139
## 300 7.4394 nan 0.0100 0.0091
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 59.6100 nan 0.0100 0.7405
## 2 58.7868 nan 0.0100 0.7641
## 3 57.9979 nan 0.0100 0.7232
## 4 57.2784 nan 0.0100 0.6819
## 5 56.6536 nan 0.0100 0.6686
## 6 55.9197 nan 0.0100 0.6963
## 7 55.1589 nan 0.0100 0.6507
## 8 54.4953 nan 0.0100 0.6717
## 9 53.8667 nan 0.0100 0.6275
## 10 53.2052 nan 0.0100 0.5868
## 20 47.3637 nan 0.0100 0.5249
## 40 38.3072 nan 0.0100 0.3386
## 60 31.4029 nan 0.0100 0.2052
## 80 26.1564 nan 0.0100 0.2181
## 100 21.9544 nan 0.0100 0.1785
## 120 18.8067 nan 0.0100 0.1058
## 140 16.3365 nan 0.0100 0.0915
## 160 14.3781 nan 0.0100 0.0808
## 180 12.7709 nan 0.0100 0.0517
## 200 11.4239 nan 0.0100 0.0570
## 220 10.3280 nan 0.0100 0.0619
## 240 9.4318 nan 0.0100 0.0239
## 260 8.6486 nan 0.0100 0.0196
## 280 7.9889 nan 0.0100 0.0245
## 300 7.4131 nan 0.0100 0.0191
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 59.6694 nan 0.0100 0.7677
## 2 58.9584 nan 0.0100 0.7666
## 3 58.1586 nan 0.0100 0.7000
## 4 57.5024 nan 0.0100 0.6179
## 5 56.8155 nan 0.0100 0.7314
## 6 56.1038 nan 0.0100 0.6908
## 7 55.3817 nan 0.0100 0.6575
## 8 54.7487 nan 0.0100 0.6695
## 9 54.0907 nan 0.0100 0.6266
## 10 53.4235 nan 0.0100 0.6263
## 20 47.6873 nan 0.0100 0.5010
## 40 38.4080 nan 0.0100 0.3874
## 60 31.3738 nan 0.0100 0.3018
## 80 26.1046 nan 0.0100 0.2256
## 100 22.0431 nan 0.0100 0.1820
## 120 18.8617 nan 0.0100 0.1221
## 140 16.3776 nan 0.0100 0.1023
## 160 14.4163 nan 0.0100 0.0582
## 180 12.8240 nan 0.0100 0.0686
## 200 11.5007 nan 0.0100 0.0537
## 220 10.3860 nan 0.0100 0.0481
## 240 9.5198 nan 0.0100 0.0288
## 260 8.7751 nan 0.0100 0.0267
## 280 8.1175 nan 0.0100 0.0248
## 300 7.5633 nan 0.0100 0.0173
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 59.4537 nan 0.0100 0.8790
## 2 58.5173 nan 0.0100 0.8837
## 3 57.5514 nan 0.0100 0.8984
## 4 56.6026 nan 0.0100 0.8982
## 5 55.6625 nan 0.0100 0.8371
## 6 54.7812 nan 0.0100 0.8885
## 7 53.8758 nan 0.0100 0.8326
## 8 53.0011 nan 0.0100 0.9246
## 9 52.1731 nan 0.0100 0.8596
## 10 51.3352 nan 0.0100 0.7578
## 20 43.6794 nan 0.0100 0.6195
## 40 32.2301 nan 0.0100 0.4822
## 60 24.2577 nan 0.0100 0.3160
## 80 18.6253 nan 0.0100 0.2283
## 100 14.6195 nan 0.0100 0.1435
## 120 11.7307 nan 0.0100 0.1277
## 140 9.5705 nan 0.0100 0.0827
## 160 8.0502 nan 0.0100 0.0475
## 180 6.9022 nan 0.0100 0.0449
## 200 6.0159 nan 0.0100 0.0300
## 220 5.3758 nan 0.0100 0.0240
## 240 4.8923 nan 0.0100 0.0122
## 260 4.5289 nan 0.0100 0.0105
## 280 4.2590 nan 0.0100 0.0054
## 300 4.0390 nan 0.0100 0.0027
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 59.4854 nan 0.0100 1.0315
## 2 58.5040 nan 0.0100 0.9146
## 3 57.5697 nan 0.0100 0.8463
## 4 56.6588 nan 0.0100 0.9269
## 5 55.7566 nan 0.0100 0.7372
## 6 54.8282 nan 0.0100 0.9267
## 7 53.9807 nan 0.0100 0.9041
## 8 53.0988 nan 0.0100 0.8590
## 9 52.2335 nan 0.0100 0.7957
## 10 51.4233 nan 0.0100 0.7834
## 20 43.8350 nan 0.0100 0.6357
## 40 32.2412 nan 0.0100 0.4445
## 60 24.1629 nan 0.0100 0.2989
## 80 18.6372 nan 0.0100 0.2468
## 100 14.6186 nan 0.0100 0.1670
## 120 11.7218 nan 0.0100 0.1286
## 140 9.6425 nan 0.0100 0.0816
## 160 8.1241 nan 0.0100 0.0562
## 180 6.9762 nan 0.0100 0.0245
## 200 6.1034 nan 0.0100 0.0351
## 220 5.4673 nan 0.0100 0.0159
## 240 4.9795 nan 0.0100 0.0145
## 260 4.5980 nan 0.0100 0.0083
## 280 4.3341 nan 0.0100 0.0057
## 300 4.1309 nan 0.0100 0.0039
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 59.3985 nan 0.0100 0.9423
## 2 58.4228 nan 0.0100 1.0260
## 3 57.4807 nan 0.0100 0.8263
## 4 56.5449 nan 0.0100 1.0227
## 5 55.6241 nan 0.0100 0.8614
## 6 54.7140 nan 0.0100 0.9334
## 7 53.8096 nan 0.0100 0.8481
## 8 53.0135 nan 0.0100 0.8680
## 9 52.1557 nan 0.0100 0.8153
## 10 51.3196 nan 0.0100 0.8642
## 20 43.6644 nan 0.0100 0.6664
## 40 32.1502 nan 0.0100 0.4758
## 60 24.3324 nan 0.0100 0.2923
## 80 18.6202 nan 0.0100 0.2149
## 100 14.6901 nan 0.0100 0.1642
## 120 11.9079 nan 0.0100 0.0740
## 140 9.9109 nan 0.0100 0.0675
## 160 8.3872 nan 0.0100 0.0491
## 180 7.2240 nan 0.0100 0.0454
## 200 6.4056 nan 0.0100 0.0321
## 220 5.7914 nan 0.0100 0.0203
## 240 5.3270 nan 0.0100 0.0153
## 260 4.9732 nan 0.0100 0.0131
## 280 4.6992 nan 0.0100 0.0058
## 300 4.4856 nan 0.0100 0.0045
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 59.3399 nan 0.0100 1.0410
## 2 58.3287 nan 0.0100 0.9834
## 3 57.2252 nan 0.0100 1.1228
## 4 56.2826 nan 0.0100 0.8837
## 5 55.2901 nan 0.0100 0.9723
## 6 54.3333 nan 0.0100 0.9093
## 7 53.3809 nan 0.0100 0.8958
## 8 52.4560 nan 0.0100 0.9086
## 9 51.6023 nan 0.0100 0.8679
## 10 50.7378 nan 0.0100 0.8417
## 20 42.8202 nan 0.0100 0.7165
## 40 30.9214 nan 0.0100 0.4491
## 60 22.7483 nan 0.0100 0.3333
## 80 16.9485 nan 0.0100 0.2021
## 100 12.8695 nan 0.0100 0.1425
## 120 10.0022 nan 0.0100 0.0966
## 140 8.0450 nan 0.0100 0.0707
## 160 6.6209 nan 0.0100 0.0568
## 180 5.5803 nan 0.0100 0.0299
## 200 4.8577 nan 0.0100 0.0196
## 220 4.3469 nan 0.0100 0.0168
## 240 3.9497 nan 0.0100 0.0069
## 260 3.6457 nan 0.0100 0.0050
## 280 3.4158 nan 0.0100 0.0049
## 300 3.2262 nan 0.0100 -0.0022
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 59.3679 nan 0.0100 1.0587
## 2 58.3027 nan 0.0100 1.0086
## 3 57.2678 nan 0.0100 1.0492
## 4 56.2529 nan 0.0100 0.9949
## 5 55.3397 nan 0.0100 1.0062
## 6 54.3528 nan 0.0100 0.8128
## 7 53.4004 nan 0.0100 0.9332
## 8 52.4631 nan 0.0100 0.9586
## 9 51.5575 nan 0.0100 0.9106
## 10 50.6794 nan 0.0100 0.9015
## 20 42.6847 nan 0.0100 0.6385
## 40 30.6552 nan 0.0100 0.5083
## 60 22.4710 nan 0.0100 0.3535
## 80 16.8240 nan 0.0100 0.2058
## 100 12.8683 nan 0.0100 0.1474
## 120 10.0577 nan 0.0100 0.1039
## 140 8.1552 nan 0.0100 0.0756
## 160 6.7648 nan 0.0100 0.0619
## 180 5.7506 nan 0.0100 0.0338
## 200 5.0544 nan 0.0100 0.0254
## 220 4.5420 nan 0.0100 0.0140
## 240 4.1540 nan 0.0100 0.0048
## 260 3.8496 nan 0.0100 0.0045
## 280 3.6260 nan 0.0100 0.0008
## 300 3.4483 nan 0.0100 0.0017
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 59.3479 nan 0.0100 1.0742
## 2 58.3655 nan 0.0100 1.0188
## 3 57.3481 nan 0.0100 0.9436
## 4 56.3870 nan 0.0100 0.9702
## 5 55.4357 nan 0.0100 0.8858
## 6 54.5114 nan 0.0100 0.8888
## 7 53.5687 nan 0.0100 0.8814
## 8 52.6887 nan 0.0100 0.8102
## 9 51.7623 nan 0.0100 0.9391
## 10 50.8459 nan 0.0100 0.7667
## 20 43.0407 nan 0.0100 0.7046
## 40 31.0539 nan 0.0100 0.5283
## 60 22.9464 nan 0.0100 0.3422
## 80 17.2216 nan 0.0100 0.2431
## 100 13.2803 nan 0.0100 0.1444
## 120 10.4537 nan 0.0100 0.1020
## 140 8.5716 nan 0.0100 0.0816
## 160 7.1649 nan 0.0100 0.0433
## 180 6.1610 nan 0.0100 0.0347
## 200 5.4470 nan 0.0100 0.0252
## 220 4.9239 nan 0.0100 0.0133
## 240 4.5613 nan 0.0100 0.0031
## 260 4.2796 nan 0.0100 0.0029
## 280 4.0610 nan 0.0100 0.0046
## 300 3.9013 nan 0.0100 0.0034
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 56.6261 nan 0.0500 3.7487
## 2 53.7912 nan 0.0500 2.7395
## 3 50.7649 nan 0.0500 3.2602
## 4 47.8375 nan 0.0500 3.0468
## 5 45.3476 nan 0.0500 2.6203
## 6 42.9063 nan 0.0500 2.3380
## 7 40.5140 nan 0.0500 2.2158
## 8 38.3436 nan 0.0500 2.0480
## 9 36.3411 nan 0.0500 2.3154
## 10 34.4406 nan 0.0500 1.6555
## 20 21.5898 nan 0.0500 0.8591
## 40 11.0134 nan 0.0500 0.2203
## 60 7.3240 nan 0.0500 0.0550
## 80 5.6272 nan 0.0500 0.0217
## 100 4.8134 nan 0.0500 0.0298
## 120 4.4378 nan 0.0500 -0.0111
## 140 4.2641 nan 0.0500 -0.0120
## 160 4.1355 nan 0.0500 -0.0072
## 180 4.0309 nan 0.0500 -0.0101
## 200 3.9671 nan 0.0500 -0.0051
## 220 3.8848 nan 0.0500 -0.0197
## 240 3.8219 nan 0.0500 -0.0204
## 260 3.7512 nan 0.0500 -0.0056
## 280 3.6941 nan 0.0500 -0.0100
## 300 3.6495 nan 0.0500 -0.0182
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 56.8668 nan 0.0500 3.5521
## 2 53.5744 nan 0.0500 3.2931
## 3 50.4557 nan 0.0500 2.8791
## 4 47.4603 nan 0.0500 2.8969
## 5 44.8641 nan 0.0500 2.4955
## 6 42.5790 nan 0.0500 2.3619
## 7 40.2903 nan 0.0500 2.1625
## 8 38.0764 nan 0.0500 1.9872
## 9 36.1914 nan 0.0500 1.7092
## 10 34.4225 nan 0.0500 1.4870
## 20 21.6748 nan 0.0500 0.8449
## 40 11.3266 nan 0.0500 0.2378
## 60 7.4220 nan 0.0500 0.0895
## 80 5.6884 nan 0.0500 0.0698
## 100 4.8437 nan 0.0500 -0.0277
## 120 4.4746 nan 0.0500 -0.0180
## 140 4.3092 nan 0.0500 -0.0166
## 160 4.1675 nan 0.0500 0.0011
## 180 4.0536 nan 0.0500 -0.0089
## 200 3.9674 nan 0.0500 0.0001
## 220 3.9136 nan 0.0500 -0.0048
## 240 3.8534 nan 0.0500 -0.0077
## 260 3.8007 nan 0.0500 -0.0060
## 280 3.7443 nan 0.0500 -0.0072
## 300 3.6985 nan 0.0500 -0.0055
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 56.5783 nan 0.0500 3.6804
## 2 53.1842 nan 0.0500 3.1426
## 3 50.0170 nan 0.0500 3.0461
## 4 47.1402 nan 0.0500 2.6536
## 5 44.5611 nan 0.0500 2.5262
## 6 42.0138 nan 0.0500 2.5241
## 7 39.7197 nan 0.0500 1.9784
## 8 37.7440 nan 0.0500 2.0082
## 9 35.8590 nan 0.0500 1.7508
## 10 34.0054 nan 0.0500 1.5630
## 20 21.5637 nan 0.0500 0.7070
## 40 11.2986 nan 0.0500 0.3113
## 60 7.4859 nan 0.0500 0.1079
## 80 5.8868 nan 0.0500 0.0385
## 100 5.1682 nan 0.0500 0.0113
## 120 4.8127 nan 0.0500 -0.0073
## 140 4.6199 nan 0.0500 0.0005
## 160 4.4894 nan 0.0500 -0.0013
## 180 4.3780 nan 0.0500 -0.0085
## 200 4.2904 nan 0.0500 -0.0062
## 220 4.2179 nan 0.0500 -0.0164
## 240 4.1445 nan 0.0500 -0.0062
## 260 4.0778 nan 0.0500 -0.0051
## 280 4.0302 nan 0.0500 -0.0204
## 300 3.9868 nan 0.0500 -0.0134
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 55.9390 nan 0.0500 4.5195
## 2 51.3501 nan 0.0500 4.3905
## 3 47.3501 nan 0.0500 3.3966
## 4 43.4784 nan 0.0500 3.4441
## 5 40.0792 nan 0.0500 3.3657
## 6 37.1872 nan 0.0500 2.7718
## 7 34.4842 nan 0.0500 2.8705
## 8 31.8459 nan 0.0500 2.2082
## 9 29.4891 nan 0.0500 2.2506
## 10 27.3931 nan 0.0500 2.0703
## 20 14.2410 nan 0.0500 0.7321
## 40 6.0520 nan 0.0500 0.1605
## 60 4.1846 nan 0.0500 0.0145
## 80 3.4837 nan 0.0500 0.0023
## 100 3.1241 nan 0.0500 -0.0041
## 120 2.8952 nan 0.0500 -0.0096
## 140 2.7131 nan 0.0500 -0.0124
## 160 2.5656 nan 0.0500 -0.0012
## 180 2.4045 nan 0.0500 -0.0087
## 200 2.2753 nan 0.0500 -0.0159
## 220 2.1630 nan 0.0500 -0.0185
## 240 2.0601 nan 0.0500 -0.0090
## 260 1.9821 nan 0.0500 -0.0217
## 280 1.8794 nan 0.0500 -0.0135
## 300 1.8027 nan 0.0500 -0.0101
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 55.7343 nan 0.0500 4.8378
## 2 51.0956 nan 0.0500 3.9425
## 3 47.1105 nan 0.0500 4.1542
## 4 43.6623 nan 0.0500 3.4586
## 5 40.3779 nan 0.0500 3.2234
## 6 37.4336 nan 0.0500 2.8433
## 7 34.6588 nan 0.0500 3.1844
## 8 32.0552 nan 0.0500 2.2884
## 9 29.5180 nan 0.0500 2.4060
## 10 27.3576 nan 0.0500 1.9554
## 20 14.0868 nan 0.0500 0.7854
## 40 6.0087 nan 0.0500 0.1386
## 60 4.0308 nan 0.0500 0.0192
## 80 3.4844 nan 0.0500 -0.0071
## 100 3.1757 nan 0.0500 0.0057
## 120 2.9451 nan 0.0500 -0.0117
## 140 2.7599 nan 0.0500 -0.0066
## 160 2.6131 nan 0.0500 -0.0043
## 180 2.4732 nan 0.0500 -0.0046
## 200 2.3528 nan 0.0500 -0.0104
## 220 2.2511 nan 0.0500 -0.0119
## 240 2.1719 nan 0.0500 -0.0064
## 260 2.0978 nan 0.0500 -0.0170
## 280 2.0266 nan 0.0500 -0.0138
## 300 1.9598 nan 0.0500 -0.0075
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 55.7425 nan 0.0500 4.6921
## 2 51.3009 nan 0.0500 4.1635
## 3 47.3791 nan 0.0500 4.1524
## 4 43.8733 nan 0.0500 3.7272
## 5 40.5440 nan 0.0500 3.4299
## 6 37.4942 nan 0.0500 2.9157
## 7 34.7647 nan 0.0500 2.5271
## 8 32.2965 nan 0.0500 2.3437
## 9 29.8578 nan 0.0500 2.4670
## 10 27.6123 nan 0.0500 1.8093
## 20 14.5650 nan 0.0500 0.7572
## 40 6.3284 nan 0.0500 0.0840
## 60 4.4073 nan 0.0500 0.0195
## 80 3.8088 nan 0.0500 0.0057
## 100 3.5520 nan 0.0500 -0.0171
## 120 3.3050 nan 0.0500 -0.0224
## 140 3.1280 nan 0.0500 -0.0052
## 160 2.9630 nan 0.0500 -0.0273
## 180 2.8684 nan 0.0500 -0.0183
## 200 2.7673 nan 0.0500 -0.0063
## 220 2.6688 nan 0.0500 -0.0161
## 240 2.5867 nan 0.0500 -0.0197
## 260 2.5087 nan 0.0500 -0.0186
## 280 2.4237 nan 0.0500 -0.0203
## 300 2.3569 nan 0.0500 -0.0124
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 55.0482 nan 0.0500 5.3768
## 2 50.5553 nan 0.0500 4.6181
## 3 46.3952 nan 0.0500 4.0937
## 4 42.3138 nan 0.0500 3.9664
## 5 38.9461 nan 0.0500 3.0012
## 6 35.8536 nan 0.0500 2.9039
## 7 33.1376 nan 0.0500 2.6641
## 8 30.4637 nan 0.0500 2.3516
## 9 28.0884 nan 0.0500 2.2834
## 10 25.8278 nan 0.0500 2.2553
## 20 12.5306 nan 0.0500 0.8718
## 40 4.6861 nan 0.0500 0.1295
## 60 3.1509 nan 0.0500 -0.0100
## 80 2.6414 nan 0.0500 -0.0136
## 100 2.3102 nan 0.0500 -0.0137
## 120 2.0831 nan 0.0500 -0.0093
## 140 1.8696 nan 0.0500 -0.0117
## 160 1.6804 nan 0.0500 -0.0140
## 180 1.5325 nan 0.0500 -0.0080
## 200 1.4241 nan 0.0500 -0.0184
## 220 1.3063 nan 0.0500 -0.0132
## 240 1.2183 nan 0.0500 -0.0078
## 260 1.1336 nan 0.0500 -0.0098
## 280 1.0626 nan 0.0500 -0.0084
## 300 0.9816 nan 0.0500 -0.0084
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 55.1561 nan 0.0500 5.0999
## 2 50.5189 nan 0.0500 4.2868
## 3 46.2841 nan 0.0500 4.5820
## 4 42.6140 nan 0.0500 3.6570
## 5 39.0915 nan 0.0500 3.3062
## 6 36.0379 nan 0.0500 3.1714
## 7 32.8576 nan 0.0500 2.9433
## 8 30.2185 nan 0.0500 2.6580
## 9 27.8724 nan 0.0500 2.4005
## 10 25.7293 nan 0.0500 1.9413
## 20 12.5623 nan 0.0500 0.7227
## 40 4.9418 nan 0.0500 0.1395
## 60 3.4713 nan 0.0500 -0.0119
## 80 2.9431 nan 0.0500 -0.0307
## 100 2.6732 nan 0.0500 -0.0293
## 120 2.4515 nan 0.0500 -0.0147
## 140 2.2972 nan 0.0500 -0.0204
## 160 2.1346 nan 0.0500 -0.0275
## 180 1.9714 nan 0.0500 -0.0118
## 200 1.8353 nan 0.0500 -0.0234
## 220 1.7369 nan 0.0500 -0.0092
## 240 1.6407 nan 0.0500 -0.0108
## 260 1.5505 nan 0.0500 -0.0085
## 280 1.4689 nan 0.0500 -0.0135
## 300 1.3887 nan 0.0500 -0.0085
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 55.0419 nan 0.0500 5.1156
## 2 50.2492 nan 0.0500 4.5451
## 3 46.2944 nan 0.0500 3.9689
## 4 42.3943 nan 0.0500 4.0668
## 5 38.9155 nan 0.0500 3.1312
## 6 35.8146 nan 0.0500 2.7849
## 7 32.8144 nan 0.0500 2.6969
## 8 30.3687 nan 0.0500 2.4560
## 9 28.0512 nan 0.0500 2.3712
## 10 25.9431 nan 0.0500 2.1032
## 20 12.7705 nan 0.0500 0.7598
## 40 5.2424 nan 0.0500 0.1559
## 60 3.8161 nan 0.0500 -0.0034
## 80 3.3745 nan 0.0500 -0.0119
## 100 3.0944 nan 0.0500 -0.0002
## 120 2.8843 nan 0.0500 -0.0151
## 140 2.7255 nan 0.0500 -0.0002
## 160 2.5864 nan 0.0500 -0.0183
## 180 2.4316 nan 0.0500 -0.0128
## 200 2.2901 nan 0.0500 -0.0063
## 220 2.1765 nan 0.0500 -0.0167
## 240 2.0934 nan 0.0500 -0.0136
## 260 2.0053 nan 0.0500 -0.0113
## 280 1.9271 nan 0.0500 -0.0223
## 300 1.8562 nan 0.0500 -0.0181
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 53.0272 nan 0.1000 7.3389
## 2 46.9954 nan 0.1000 5.7975
## 3 42.0666 nan 0.1000 4.8178
## 4 37.9822 nan 0.1000 4.1102
## 5 34.1106 nan 0.1000 3.7522
## 6 31.0223 nan 0.1000 2.7855
## 7 27.8301 nan 0.1000 2.7865
## 8 25.4326 nan 0.1000 2.3715
## 9 23.6111 nan 0.1000 1.9434
## 10 21.8393 nan 0.1000 1.5203
## 20 11.2842 nan 0.1000 0.4453
## 40 5.8421 nan 0.1000 0.0947
## 60 4.7185 nan 0.1000 0.0019
## 80 4.4229 nan 0.1000 -0.0531
## 100 4.2245 nan 0.1000 -0.0037
## 120 4.0512 nan 0.1000 -0.0104
## 140 3.9244 nan 0.1000 -0.0365
## 160 3.8124 nan 0.1000 -0.0235
## 180 3.7065 nan 0.1000 -0.0178
## 200 3.6129 nan 0.1000 -0.0115
## 220 3.5099 nan 0.1000 -0.0344
## 240 3.4259 nan 0.1000 -0.0262
## 260 3.3584 nan 0.1000 -0.0170
## 280 3.2961 nan 0.1000 -0.0191
## 300 3.2404 nan 0.1000 -0.0209
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 53.0410 nan 0.1000 6.9696
## 2 46.7956 nan 0.1000 5.8817
## 3 41.3360 nan 0.1000 5.2099
## 4 36.7949 nan 0.1000 3.7840
## 5 33.6464 nan 0.1000 3.1577
## 6 30.4385 nan 0.1000 2.9681
## 7 27.8181 nan 0.1000 2.5351
## 8 24.9692 nan 0.1000 2.3506
## 9 22.7097 nan 0.1000 2.1194
## 10 20.8603 nan 0.1000 1.9698
## 20 11.0603 nan 0.1000 0.5672
## 40 5.5132 nan 0.1000 0.0774
## 60 4.5051 nan 0.1000 0.0077
## 80 4.1886 nan 0.1000 -0.0056
## 100 4.0222 nan 0.1000 -0.0118
## 120 3.8851 nan 0.1000 -0.0125
## 140 3.7724 nan 0.1000 -0.0125
## 160 3.6565 nan 0.1000 -0.0092
## 180 3.5716 nan 0.1000 -0.0101
## 200 3.4841 nan 0.1000 -0.0455
## 220 3.4304 nan 0.1000 -0.0054
## 240 3.3541 nan 0.1000 -0.0091
## 260 3.3104 nan 0.1000 -0.0223
## 280 3.2504 nan 0.1000 -0.0040
## 300 3.2099 nan 0.1000 -0.0253
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 53.0709 nan 0.1000 7.3321
## 2 47.0443 nan 0.1000 5.4503
## 3 42.0566 nan 0.1000 5.1609
## 4 37.6932 nan 0.1000 4.1360
## 5 33.9424 nan 0.1000 3.7307
## 6 30.0233 nan 0.1000 3.1631
## 7 27.3552 nan 0.1000 2.6061
## 8 24.9600 nan 0.1000 1.9477
## 9 22.6653 nan 0.1000 1.9668
## 10 20.9076 nan 0.1000 1.4590
## 20 11.3000 nan 0.1000 0.2648
## 40 6.1307 nan 0.1000 0.0326
## 60 5.0579 nan 0.1000 0.0327
## 80 4.7267 nan 0.1000 -0.0030
## 100 4.4504 nan 0.1000 -0.0164
## 120 4.3004 nan 0.1000 -0.0130
## 140 4.1323 nan 0.1000 -0.0222
## 160 4.0080 nan 0.1000 -0.0376
## 180 3.9045 nan 0.1000 -0.0196
## 200 3.8153 nan 0.1000 -0.0240
## 220 3.7631 nan 0.1000 -0.0191
## 240 3.6920 nan 0.1000 -0.0248
## 260 3.6260 nan 0.1000 -0.0014
## 280 3.5905 nan 0.1000 -0.0302
## 300 3.5211 nan 0.1000 -0.0181
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 50.7248 nan 0.1000 9.2858
## 2 43.2063 nan 0.1000 7.5471
## 3 36.5006 nan 0.1000 5.7241
## 4 31.2351 nan 0.1000 4.8840
## 5 26.7152 nan 0.1000 4.2072
## 6 23.0201 nan 0.1000 3.4714
## 7 20.1231 nan 0.1000 2.4880
## 8 17.5469 nan 0.1000 2.6343
## 9 15.6246 nan 0.1000 1.5836
## 10 13.8156 nan 0.1000 1.7091
## 20 5.7140 nan 0.1000 0.3476
## 40 3.4255 nan 0.1000 -0.0247
## 60 2.8808 nan 0.1000 -0.0209
## 80 2.5487 nan 0.1000 -0.0336
## 100 2.2685 nan 0.1000 -0.0374
## 120 2.0651 nan 0.1000 -0.0084
## 140 1.8614 nan 0.1000 -0.0207
## 160 1.7001 nan 0.1000 -0.0309
## 180 1.5659 nan 0.1000 -0.0292
## 200 1.4643 nan 0.1000 -0.0334
## 220 1.3380 nan 0.1000 -0.0175
## 240 1.2432 nan 0.1000 -0.0182
## 260 1.1622 nan 0.1000 -0.0081
## 280 1.1045 nan 0.1000 -0.0181
## 300 1.0439 nan 0.1000 -0.0124
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 50.4646 nan 0.1000 8.9282
## 2 42.9349 nan 0.1000 7.7266
## 3 36.5747 nan 0.1000 6.5063
## 4 31.2569 nan 0.1000 5.3462
## 5 27.1363 nan 0.1000 4.5185
## 6 23.5290 nan 0.1000 3.6736
## 7 20.5851 nan 0.1000 2.6638
## 8 18.0168 nan 0.1000 1.9615
## 9 15.7701 nan 0.1000 2.1022
## 10 13.9885 nan 0.1000 1.7248
## 20 5.9226 nan 0.1000 0.2677
## 40 3.6319 nan 0.1000 0.0126
## 60 3.0749 nan 0.1000 -0.0128
## 80 2.7207 nan 0.1000 -0.0233
## 100 2.5197 nan 0.1000 -0.0504
## 120 2.3332 nan 0.1000 -0.0350
## 140 2.1537 nan 0.1000 -0.0299
## 160 2.0318 nan 0.1000 -0.0123
## 180 1.9086 nan 0.1000 -0.0406
## 200 1.8073 nan 0.1000 -0.0197
## 220 1.7195 nan 0.1000 -0.0087
## 240 1.6083 nan 0.1000 -0.0175
## 260 1.5153 nan 0.1000 -0.0226
## 280 1.4391 nan 0.1000 -0.0261
## 300 1.3732 nan 0.1000 -0.0259
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 51.4166 nan 0.1000 8.9886
## 2 42.9857 nan 0.1000 7.5408
## 3 36.7442 nan 0.1000 5.7071
## 4 31.5188 nan 0.1000 5.3673
## 5 27.2102 nan 0.1000 3.8222
## 6 23.7300 nan 0.1000 3.4089
## 7 20.6408 nan 0.1000 2.8187
## 8 18.1909 nan 0.1000 2.4432
## 9 16.0387 nan 0.1000 2.3327
## 10 14.2283 nan 0.1000 1.6718
## 20 6.0296 nan 0.1000 0.2584
## 40 3.8770 nan 0.1000 -0.0702
## 60 3.3455 nan 0.1000 -0.0235
## 80 3.0897 nan 0.1000 -0.0296
## 100 2.8656 nan 0.1000 -0.0167
## 120 2.6807 nan 0.1000 -0.0168
## 140 2.5298 nan 0.1000 -0.0186
## 160 2.4246 nan 0.1000 -0.0132
## 180 2.2800 nan 0.1000 -0.0371
## 200 2.1757 nan 0.1000 -0.0113
## 220 2.0629 nan 0.1000 -0.0145
## 240 1.9731 nan 0.1000 -0.0129
## 260 1.8797 nan 0.1000 -0.0194
## 280 1.7918 nan 0.1000 -0.0259
## 300 1.7220 nan 0.1000 -0.0282
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 50.0360 nan 0.1000 9.5484
## 2 41.8452 nan 0.1000 7.7112
## 3 35.1514 nan 0.1000 6.1821
## 4 29.7073 nan 0.1000 4.9340
## 5 25.0406 nan 0.1000 4.3463
## 6 21.0266 nan 0.1000 3.5775
## 7 17.9713 nan 0.1000 2.7707
## 8 15.7020 nan 0.1000 2.0902
## 9 13.5466 nan 0.1000 2.1521
## 10 11.8641 nan 0.1000 1.6025
## 20 4.5786 nan 0.1000 0.1816
## 40 2.6642 nan 0.1000 -0.0197
## 60 2.1329 nan 0.1000 -0.0342
## 80 1.7763 nan 0.1000 -0.0151
## 100 1.4969 nan 0.1000 -0.0152
## 120 1.2975 nan 0.1000 -0.0166
## 140 1.1381 nan 0.1000 -0.0213
## 160 0.9880 nan 0.1000 -0.0128
## 180 0.8813 nan 0.1000 -0.0133
## 200 0.7783 nan 0.1000 -0.0059
## 220 0.6910 nan 0.1000 -0.0115
## 240 0.6224 nan 0.1000 -0.0082
## 260 0.5610 nan 0.1000 -0.0076
## 280 0.5034 nan 0.1000 -0.0080
## 300 0.4527 nan 0.1000 -0.0074
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 50.2778 nan 0.1000 8.9345
## 2 41.7666 nan 0.1000 7.7506
## 3 34.9156 nan 0.1000 6.8147
## 4 29.5401 nan 0.1000 5.6076
## 5 25.0086 nan 0.1000 4.5469
## 6 21.6057 nan 0.1000 3.8313
## 7 18.7128 nan 0.1000 3.0627
## 8 16.1744 nan 0.1000 2.2358
## 9 14.0793 nan 0.1000 2.0603
## 10 12.4610 nan 0.1000 1.1170
## 20 4.8868 nan 0.1000 0.2328
## 40 3.0229 nan 0.1000 -0.0134
## 60 2.5680 nan 0.1000 -0.0368
## 80 2.2054 nan 0.1000 -0.0141
## 100 1.9311 nan 0.1000 -0.0270
## 120 1.7509 nan 0.1000 -0.0236
## 140 1.6078 nan 0.1000 -0.0137
## 160 1.4672 nan 0.1000 -0.0156
## 180 1.3258 nan 0.1000 -0.0185
## 200 1.2247 nan 0.1000 -0.0197
## 220 1.1190 nan 0.1000 -0.0224
## 240 1.0206 nan 0.1000 -0.0170
## 260 0.9396 nan 0.1000 -0.0046
## 280 0.8615 nan 0.1000 -0.0167
## 300 0.7981 nan 0.1000 -0.0155
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 50.4441 nan 0.1000 8.8635
## 2 41.9766 nan 0.1000 7.6464
## 3 35.3747 nan 0.1000 6.3424
## 4 29.7385 nan 0.1000 5.3254
## 5 25.3737 nan 0.1000 4.7341
## 6 21.7400 nan 0.1000 3.3840
## 7 18.6101 nan 0.1000 2.9630
## 8 16.2892 nan 0.1000 2.2019
## 9 14.0839 nan 0.1000 2.0074
## 10 12.3625 nan 0.1000 1.5485
## 20 5.4973 nan 0.1000 0.1885
## 40 3.5820 nan 0.1000 -0.0063
## 60 3.1146 nan 0.1000 -0.0166
## 80 2.7698 nan 0.1000 -0.0601
## 100 2.4958 nan 0.1000 -0.0410
## 120 2.2761 nan 0.1000 -0.0009
## 140 2.0761 nan 0.1000 -0.0264
## 160 1.9385 nan 0.1000 -0.0237
## 180 1.7833 nan 0.1000 -0.0195
## 200 1.6533 nan 0.1000 -0.0340
## 220 1.5265 nan 0.1000 -0.0185
## 240 1.4255 nan 0.1000 -0.0151
## 260 1.3589 nan 0.1000 -0.0171
## 280 1.2813 nan 0.1000 -0.0090
## 300 1.2163 nan 0.1000 -0.0220
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 61.2471 nan 0.0100 0.8171
## 2 60.4397 nan 0.0100 0.7450
## 3 59.7327 nan 0.0100 0.6858
## 4 59.0314 nan 0.0100 0.7008
## 5 58.3337 nan 0.0100 0.7113
## 6 57.6094 nan 0.0100 0.7210
## 7 56.9120 nan 0.0100 0.6757
## 8 56.2622 nan 0.0100 0.7056
## 9 55.5959 nan 0.0100 0.6775
## 10 54.9864 nan 0.0100 0.6378
## 20 48.8743 nan 0.0100 0.5066
## 40 39.3633 nan 0.0100 0.3880
## 60 32.1468 nan 0.0100 0.3041
## 80 26.8350 nan 0.0100 0.2220
## 100 22.6433 nan 0.0100 0.1638
## 120 19.5177 nan 0.0100 0.1366
## 140 16.9959 nan 0.0100 0.1089
## 160 14.9314 nan 0.0100 0.0922
## 180 13.3013 nan 0.0100 0.0675
## 200 11.8810 nan 0.0100 0.0592
## 220 10.7510 nan 0.0100 0.0419
## 240 9.8464 nan 0.0100 0.0284
## 260 9.0426 nan 0.0100 0.0322
## 280 8.3754 nan 0.0100 0.0153
## 300 7.8088 nan 0.0100 0.0213
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 61.2335 nan 0.0100 0.7930
## 2 60.4126 nan 0.0100 0.7724
## 3 59.7121 nan 0.0100 0.7442
## 4 58.9596 nan 0.0100 0.7410
## 5 58.3147 nan 0.0100 0.6368
## 6 57.6453 nan 0.0100 0.7391
## 7 56.9562 nan 0.0100 0.6340
## 8 56.2538 nan 0.0100 0.6613
## 9 55.6347 nan 0.0100 0.6316
## 10 54.9292 nan 0.0100 0.6321
## 20 48.9372 nan 0.0100 0.5727
## 40 39.3745 nan 0.0100 0.3718
## 60 32.2644 nan 0.0100 0.3136
## 80 27.0154 nan 0.0100 0.2265
## 100 22.8407 nan 0.0100 0.1654
## 120 19.6301 nan 0.0100 0.1356
## 140 17.1105 nan 0.0100 0.1159
## 160 15.1062 nan 0.0100 0.0714
## 180 13.4058 nan 0.0100 0.0683
## 200 12.0099 nan 0.0100 0.0494
## 220 10.8768 nan 0.0100 0.0423
## 240 9.9340 nan 0.0100 0.0393
## 260 9.1577 nan 0.0100 0.0235
## 280 8.4650 nan 0.0100 0.0267
## 300 7.8962 nan 0.0100 0.0230
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 61.2664 nan 0.0100 0.7512
## 2 60.4755 nan 0.0100 0.7611
## 3 59.7056 nan 0.0100 0.7099
## 4 58.9449 nan 0.0100 0.7729
## 5 58.1742 nan 0.0100 0.7191
## 6 57.4567 nan 0.0100 0.6564
## 7 56.7102 nan 0.0100 0.6633
## 8 56.0161 nan 0.0100 0.6412
## 9 55.3484 nan 0.0100 0.6663
## 10 54.7126 nan 0.0100 0.6485
## 20 48.7478 nan 0.0100 0.5185
## 40 39.5092 nan 0.0100 0.3679
## 60 32.4741 nan 0.0100 0.3224
## 80 26.9540 nan 0.0100 0.1804
## 100 22.8327 nan 0.0100 0.1534
## 120 19.5640 nan 0.0100 0.1331
## 140 17.0115 nan 0.0100 0.0998
## 160 15.0070 nan 0.0100 0.0783
## 180 13.3386 nan 0.0100 0.0634
## 200 12.0034 nan 0.0100 0.0506
## 220 10.8655 nan 0.0100 0.0446
## 240 9.9849 nan 0.0100 0.0375
## 260 9.1796 nan 0.0100 0.0233
## 280 8.5283 nan 0.0100 0.0246
## 300 7.9556 nan 0.0100 0.0140
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 61.0756 nan 0.0100 1.0436
## 2 60.1121 nan 0.0100 0.9241
## 3 59.1398 nan 0.0100 0.8767
## 4 58.2601 nan 0.0100 0.9505
## 5 57.2728 nan 0.0100 0.8786
## 6 56.3825 nan 0.0100 0.8443
## 7 55.4643 nan 0.0100 0.9856
## 8 54.5975 nan 0.0100 0.9066
## 9 53.7411 nan 0.0100 0.9248
## 10 52.9454 nan 0.0100 0.8292
## 20 45.2351 nan 0.0100 0.6602
## 40 33.4845 nan 0.0100 0.4471
## 60 25.2464 nan 0.0100 0.3424
## 80 19.4439 nan 0.0100 0.2407
## 100 15.3330 nan 0.0100 0.1572
## 120 12.3388 nan 0.0100 0.0974
## 140 10.1592 nan 0.0100 0.0789
## 160 8.5966 nan 0.0100 0.0510
## 180 7.4095 nan 0.0100 0.0269
## 200 6.5071 nan 0.0100 0.0290
## 220 5.8273 nan 0.0100 0.0229
## 240 5.3414 nan 0.0100 0.0138
## 260 4.9522 nan 0.0100 0.0146
## 280 4.6608 nan 0.0100 0.0052
## 300 4.4169 nan 0.0100 0.0032
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 61.0249 nan 0.0100 1.0083
## 2 60.0501 nan 0.0100 0.8580
## 3 59.0255 nan 0.0100 1.0498
## 4 58.0567 nan 0.0100 0.8689
## 5 57.1328 nan 0.0100 0.9212
## 6 56.2389 nan 0.0100 0.9170
## 7 55.3481 nan 0.0100 0.8473
## 8 54.4487 nan 0.0100 0.8020
## 9 53.5667 nan 0.0100 0.7988
## 10 52.7073 nan 0.0100 0.7873
## 20 45.0638 nan 0.0100 0.6397
## 40 33.1983 nan 0.0100 0.4709
## 60 25.0138 nan 0.0100 0.3496
## 80 19.3344 nan 0.0100 0.2313
## 100 15.2786 nan 0.0100 0.1473
## 120 12.3923 nan 0.0100 0.1116
## 140 10.2626 nan 0.0100 0.0945
## 160 8.6657 nan 0.0100 0.0637
## 180 7.4821 nan 0.0100 0.0394
## 200 6.5980 nan 0.0100 0.0290
## 220 5.9308 nan 0.0100 0.0250
## 240 5.4515 nan 0.0100 0.0131
## 260 5.0887 nan 0.0100 0.0133
## 280 4.8044 nan 0.0100 0.0117
## 300 4.5858 nan 0.0100 0.0042
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 61.0146 nan 0.0100 1.1238
## 2 60.0157 nan 0.0100 1.1308
## 3 59.0649 nan 0.0100 1.0358
## 4 58.0879 nan 0.0100 0.8187
## 5 57.2114 nan 0.0100 0.8622
## 6 56.2606 nan 0.0100 0.9823
## 7 55.3185 nan 0.0100 0.8737
## 8 54.4709 nan 0.0100 0.8420
## 9 53.5584 nan 0.0100 0.8816
## 10 52.7162 nan 0.0100 0.8105
## 20 45.0614 nan 0.0100 0.6640
## 40 33.2360 nan 0.0100 0.4679
## 60 25.1540 nan 0.0100 0.3489
## 80 19.4696 nan 0.0100 0.2371
## 100 15.3101 nan 0.0100 0.1908
## 120 12.4223 nan 0.0100 0.1178
## 140 10.3664 nan 0.0100 0.0680
## 160 8.8373 nan 0.0100 0.0644
## 180 7.6617 nan 0.0100 0.0468
## 200 6.8181 nan 0.0100 0.0314
## 220 6.1964 nan 0.0100 0.0243
## 240 5.6959 nan 0.0100 0.0157
## 260 5.3067 nan 0.0100 0.0114
## 280 5.0145 nan 0.0100 0.0126
## 300 4.8007 nan 0.0100 0.0003
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 61.0165 nan 0.0100 1.0477
## 2 59.9716 nan 0.0100 1.0479
## 3 58.9321 nan 0.0100 1.0314
## 4 57.9297 nan 0.0100 1.0420
## 5 56.8560 nan 0.0100 1.0030
## 6 55.8343 nan 0.0100 1.1082
## 7 54.9082 nan 0.0100 0.9856
## 8 53.9656 nan 0.0100 0.8256
## 9 53.0861 nan 0.0100 0.8650
## 10 52.1747 nan 0.0100 0.8640
## 20 44.1799 nan 0.0100 0.7290
## 40 31.8072 nan 0.0100 0.5093
## 60 23.4430 nan 0.0100 0.3150
## 80 17.5244 nan 0.0100 0.2309
## 100 13.4227 nan 0.0100 0.1642
## 120 10.5454 nan 0.0100 0.1007
## 140 8.5015 nan 0.0100 0.0850
## 160 7.0931 nan 0.0100 0.0511
## 180 6.0581 nan 0.0100 0.0393
## 200 5.3591 nan 0.0100 0.0243
## 220 4.8015 nan 0.0100 0.0149
## 240 4.4036 nan 0.0100 0.0094
## 260 4.0873 nan 0.0100 0.0070
## 280 3.8529 nan 0.0100 -0.0023
## 300 3.6627 nan 0.0100 -0.0017
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 61.0055 nan 0.0100 1.0640
## 2 59.9550 nan 0.0100 1.0846
## 3 58.9050 nan 0.0100 1.0082
## 4 57.8709 nan 0.0100 0.9601
## 5 56.8651 nan 0.0100 1.0693
## 6 55.8442 nan 0.0100 0.9167
## 7 54.8700 nan 0.0100 0.8096
## 8 53.9720 nan 0.0100 0.8210
## 9 53.0454 nan 0.0100 0.8885
## 10 52.1660 nan 0.0100 0.8323
## 20 44.0084 nan 0.0100 0.6535
## 40 31.7890 nan 0.0100 0.5025
## 60 23.3379 nan 0.0100 0.3161
## 80 17.6059 nan 0.0100 0.2064
## 100 13.5621 nan 0.0100 0.1660
## 120 10.6917 nan 0.0100 0.1144
## 140 8.7166 nan 0.0100 0.0837
## 160 7.3243 nan 0.0100 0.0543
## 180 6.3081 nan 0.0100 0.0295
## 200 5.5555 nan 0.0100 0.0215
## 220 5.0228 nan 0.0100 0.0170
## 240 4.6105 nan 0.0100 0.0076
## 260 4.3088 nan 0.0100 0.0008
## 280 4.0892 nan 0.0100 0.0038
## 300 3.9008 nan 0.0100 -0.0003
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 60.9951 nan 0.0100 0.9559
## 2 59.9393 nan 0.0100 0.9891
## 3 58.8988 nan 0.0100 0.9849
## 4 57.8277 nan 0.0100 1.1098
## 5 56.8630 nan 0.0100 1.0348
## 6 55.8554 nan 0.0100 0.9254
## 7 54.9125 nan 0.0100 0.9681
## 8 53.9700 nan 0.0100 0.7811
## 9 53.1000 nan 0.0100 0.8305
## 10 52.2293 nan 0.0100 0.8243
## 20 44.1124 nan 0.0100 0.7188
## 40 31.9684 nan 0.0100 0.4830
## 60 23.5360 nan 0.0100 0.3569
## 80 17.7766 nan 0.0100 0.2328
## 100 13.7659 nan 0.0100 0.1549
## 120 10.9368 nan 0.0100 0.1074
## 140 8.9749 nan 0.0100 0.0610
## 160 7.5791 nan 0.0100 0.0477
## 180 6.5736 nan 0.0100 0.0359
## 200 5.8515 nan 0.0100 0.0259
## 220 5.3452 nan 0.0100 0.0110
## 240 4.9590 nan 0.0100 0.0148
## 260 4.6770 nan 0.0100 0.0030
## 280 4.4678 nan 0.0100 0.0046
## 300 4.2982 nan 0.0100 0.0022
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 58.2510 nan 0.0500 3.8659
## 2 54.9483 nan 0.0500 3.2659
## 3 51.7792 nan 0.0500 3.0892
## 4 48.8502 nan 0.0500 2.9909
## 5 46.2271 nan 0.0500 2.5995
## 6 43.8243 nan 0.0500 2.3293
## 7 41.3860 nan 0.0500 2.3403
## 8 39.3449 nan 0.0500 2.0760
## 9 37.2151 nan 0.0500 2.0170
## 10 35.4728 nan 0.0500 1.5757
## 20 23.2106 nan 0.0500 0.8434
## 40 12.2243 nan 0.0500 0.2998
## 60 7.9661 nan 0.0500 0.1322
## 80 6.0688 nan 0.0500 -0.0020
## 100 5.1843 nan 0.0500 0.0107
## 120 4.7871 nan 0.0500 0.0097
## 140 4.6039 nan 0.0500 -0.0185
## 160 4.4874 nan 0.0500 -0.0098
## 180 4.3956 nan 0.0500 -0.0054
## 200 4.3290 nan 0.0500 -0.0157
## 220 4.2400 nan 0.0500 -0.0015
## 240 4.1610 nan 0.0500 -0.0035
## 260 4.0935 nan 0.0500 -0.0111
## 280 4.0350 nan 0.0500 -0.0390
## 300 3.9945 nan 0.0500 -0.0038
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 58.3960 nan 0.0500 3.8379
## 2 54.9671 nan 0.0500 3.4623
## 3 51.4929 nan 0.0500 3.0140
## 4 48.6050 nan 0.0500 2.8483
## 5 45.9079 nan 0.0500 2.4888
## 6 43.7300 nan 0.0500 2.2019
## 7 41.0662 nan 0.0500 2.1655
## 8 38.7998 nan 0.0500 1.8705
## 9 36.9989 nan 0.0500 1.8070
## 10 35.3301 nan 0.0500 1.7297
## 20 22.1881 nan 0.0500 0.8367
## 40 11.8501 nan 0.0500 0.1725
## 60 7.7668 nan 0.0500 0.1199
## 80 6.0438 nan 0.0500 0.0432
## 100 5.2471 nan 0.0500 -0.0102
## 120 4.8393 nan 0.0500 -0.0134
## 140 4.6629 nan 0.0500 -0.0027
## 160 4.5407 nan 0.0500 -0.0091
## 180 4.4433 nan 0.0500 -0.0095
## 200 4.3598 nan 0.0500 -0.0017
## 220 4.3072 nan 0.0500 0.0003
## 240 4.2494 nan 0.0500 -0.0223
## 260 4.1912 nan 0.0500 -0.0101
## 280 4.1356 nan 0.0500 -0.0086
## 300 4.0780 nan 0.0500 -0.0042
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 58.0784 nan 0.0500 4.1658
## 2 54.5911 nan 0.0500 3.2199
## 3 51.4127 nan 0.0500 3.0460
## 4 48.4753 nan 0.0500 2.9579
## 5 45.7399 nan 0.0500 2.4592
## 6 43.2509 nan 0.0500 2.4544
## 7 41.0372 nan 0.0500 2.1524
## 8 39.1829 nan 0.0500 1.9616
## 9 37.3444 nan 0.0500 1.7148
## 10 35.6296 nan 0.0500 1.7590
## 20 22.9508 nan 0.0500 0.5356
## 40 11.7975 nan 0.0500 0.2155
## 60 8.0006 nan 0.0500 0.0976
## 80 6.1805 nan 0.0500 0.0126
## 100 5.3689 nan 0.0500 -0.0095
## 120 5.0349 nan 0.0500 -0.0294
## 140 4.8268 nan 0.0500 -0.0081
## 160 4.7045 nan 0.0500 -0.0124
## 180 4.6158 nan 0.0500 -0.0149
## 200 4.5387 nan 0.0500 -0.0047
## 220 4.4880 nan 0.0500 -0.0078
## 240 4.4157 nan 0.0500 -0.0124
## 260 4.3487 nan 0.0500 -0.0098
## 280 4.3108 nan 0.0500 -0.0146
## 300 4.2531 nan 0.0500 -0.0117
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 57.0509 nan 0.0500 4.9164
## 2 52.6697 nan 0.0500 4.1244
## 3 48.5121 nan 0.0500 3.7657
## 4 44.9575 nan 0.0500 3.6333
## 5 41.7553 nan 0.0500 3.3531
## 6 38.5213 nan 0.0500 3.1923
## 7 35.5831 nan 0.0500 3.0295
## 8 33.0286 nan 0.0500 2.5774
## 9 30.6986 nan 0.0500 2.1684
## 10 28.7020 nan 0.0500 2.1467
## 20 15.1303 nan 0.0500 0.8677
## 40 6.5465 nan 0.0500 0.1700
## 60 4.4643 nan 0.0500 0.0398
## 80 3.7855 nan 0.0500 -0.0092
## 100 3.3733 nan 0.0500 -0.0265
## 120 3.1284 nan 0.0500 -0.0170
## 140 2.9125 nan 0.0500 -0.0167
## 160 2.7485 nan 0.0500 -0.0306
## 180 2.5813 nan 0.0500 -0.0099
## 200 2.4451 nan 0.0500 -0.0085
## 220 2.3395 nan 0.0500 -0.0169
## 240 2.2064 nan 0.0500 -0.0071
## 260 2.1134 nan 0.0500 -0.0148
## 280 2.0193 nan 0.0500 -0.0121
## 300 1.9274 nan 0.0500 -0.0259
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 56.9425 nan 0.0500 5.1620
## 2 52.3220 nan 0.0500 4.1111
## 3 48.0163 nan 0.0500 3.8190
## 4 44.2704 nan 0.0500 3.5705
## 5 40.9959 nan 0.0500 3.4008
## 6 37.8627 nan 0.0500 2.8173
## 7 35.1591 nan 0.0500 2.6522
## 8 32.5420 nan 0.0500 2.3474
## 9 30.0371 nan 0.0500 2.2974
## 10 27.8753 nan 0.0500 1.7058
## 20 14.6966 nan 0.0500 0.8123
## 40 6.5102 nan 0.0500 0.1154
## 60 4.5803 nan 0.0500 0.0179
## 80 3.9867 nan 0.0500 -0.0312
## 100 3.6925 nan 0.0500 -0.0373
## 120 3.4431 nan 0.0500 -0.0100
## 140 3.2715 nan 0.0500 -0.0312
## 160 3.1237 nan 0.0500 -0.0117
## 180 2.9960 nan 0.0500 -0.0188
## 200 2.9043 nan 0.0500 -0.0278
## 220 2.7744 nan 0.0500 -0.0157
## 240 2.6667 nan 0.0500 -0.0303
## 260 2.5674 nan 0.0500 -0.0095
## 280 2.4871 nan 0.0500 -0.0188
## 300 2.3901 nan 0.0500 -0.0260
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 57.0138 nan 0.0500 4.8806
## 2 52.4046 nan 0.0500 4.3052
## 3 48.1771 nan 0.0500 4.0895
## 4 44.6355 nan 0.0500 3.7533
## 5 41.2741 nan 0.0500 2.9681
## 6 38.2607 nan 0.0500 2.8724
## 7 35.4450 nan 0.0500 2.7316
## 8 32.8851 nan 0.0500 2.1201
## 9 30.5620 nan 0.0500 2.0669
## 10 28.5482 nan 0.0500 1.9160
## 20 15.4266 nan 0.0500 0.7355
## 40 6.7283 nan 0.0500 0.1622
## 60 4.7553 nan 0.0500 -0.0083
## 80 4.1564 nan 0.0500 -0.0050
## 100 3.8614 nan 0.0500 -0.0238
## 120 3.6413 nan 0.0500 -0.0073
## 140 3.5058 nan 0.0500 -0.0266
## 160 3.3426 nan 0.0500 -0.0188
## 180 3.2226 nan 0.0500 -0.0181
## 200 3.1103 nan 0.0500 -0.0129
## 220 3.0073 nan 0.0500 -0.0249
## 240 2.9350 nan 0.0500 -0.0097
## 260 2.8251 nan 0.0500 -0.0103
## 280 2.7574 nan 0.0500 -0.0075
## 300 2.6761 nan 0.0500 -0.0163
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 56.8288 nan 0.0500 5.2259
## 2 52.1941 nan 0.0500 4.6416
## 3 48.1045 nan 0.0500 4.0980
## 4 44.0999 nan 0.0500 3.3660
## 5 40.4685 nan 0.0500 3.2812
## 6 37.2454 nan 0.0500 3.6083
## 7 34.1335 nan 0.0500 2.8631
## 8 31.5240 nan 0.0500 2.4587
## 9 29.1742 nan 0.0500 2.1428
## 10 26.8730 nan 0.0500 2.1001
## 20 13.4004 nan 0.0500 0.8190
## 40 5.2974 nan 0.0500 0.1081
## 60 3.6409 nan 0.0500 -0.0084
## 80 3.0458 nan 0.0500 -0.0107
## 100 2.7261 nan 0.0500 -0.0317
## 120 2.4446 nan 0.0500 -0.0325
## 140 2.2115 nan 0.0500 -0.0162
## 160 1.9837 nan 0.0500 -0.0142
## 180 1.8272 nan 0.0500 -0.0178
## 200 1.6830 nan 0.0500 -0.0170
## 220 1.5669 nan 0.0500 -0.0166
## 240 1.4746 nan 0.0500 -0.0131
## 260 1.3603 nan 0.0500 -0.0207
## 280 1.2661 nan 0.0500 -0.0174
## 300 1.1680 nan 0.0500 -0.0056
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 56.8123 nan 0.0500 5.3557
## 2 51.8963 nan 0.0500 4.3317
## 3 47.6316 nan 0.0500 4.3542
## 4 43.8881 nan 0.0500 3.9939
## 5 40.4536 nan 0.0500 3.7371
## 6 37.3856 nan 0.0500 2.9395
## 7 34.5803 nan 0.0500 3.0939
## 8 31.7217 nan 0.0500 2.3956
## 9 29.3008 nan 0.0500 2.3267
## 10 27.2113 nan 0.0500 2.0625
## 20 13.3472 nan 0.0500 0.8152
## 40 5.4916 nan 0.0500 0.1057
## 60 3.9257 nan 0.0500 0.0078
## 80 3.3900 nan 0.0500 -0.0255
## 100 3.0654 nan 0.0500 -0.0153
## 120 2.7759 nan 0.0500 -0.0105
## 140 2.6206 nan 0.0500 -0.0235
## 160 2.4814 nan 0.0500 -0.0235
## 180 2.3341 nan 0.0500 -0.0282
## 200 2.1656 nan 0.0500 -0.0143
## 220 2.0448 nan 0.0500 -0.0162
## 240 1.9491 nan 0.0500 -0.0229
## 260 1.8512 nan 0.0500 -0.0216
## 280 1.7562 nan 0.0500 -0.0140
## 300 1.6646 nan 0.0500 -0.0048
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 56.7805 nan 0.0500 5.1416
## 2 52.0776 nan 0.0500 4.5647
## 3 47.6773 nan 0.0500 4.3866
## 4 43.7995 nan 0.0500 4.0749
## 5 40.1579 nan 0.0500 3.3395
## 6 36.9355 nan 0.0500 3.0672
## 7 33.9534 nan 0.0500 2.8057
## 8 31.3132 nan 0.0500 2.2860
## 9 28.8416 nan 0.0500 2.1411
## 10 26.7253 nan 0.0500 1.8991
## 20 13.3205 nan 0.0500 0.7813
## 40 5.7091 nan 0.0500 0.1037
## 60 4.2640 nan 0.0500 -0.0049
## 80 3.8119 nan 0.0500 -0.0376
## 100 3.5128 nan 0.0500 -0.0240
## 120 3.3067 nan 0.0500 -0.0220
## 140 3.1178 nan 0.0500 -0.0032
## 160 2.9730 nan 0.0500 -0.0221
## 180 2.8217 nan 0.0500 -0.0100
## 200 2.6794 nan 0.0500 -0.0087
## 220 2.5736 nan 0.0500 -0.0121
## 240 2.4393 nan 0.0500 -0.0119
## 260 2.3314 nan 0.0500 -0.0181
## 280 2.2384 nan 0.0500 -0.0124
## 300 2.1517 nan 0.0500 -0.0140
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 54.9715 nan 0.1000 7.0198
## 2 48.7284 nan 0.1000 6.0682
## 3 43.0850 nan 0.1000 4.6082
## 4 38.6990 nan 0.1000 4.1440
## 5 34.9746 nan 0.1000 3.3631
## 6 31.3431 nan 0.1000 3.2554
## 7 28.5754 nan 0.1000 2.5183
## 8 25.8712 nan 0.1000 2.0595
## 9 24.1180 nan 0.1000 1.6862
## 10 22.5115 nan 0.1000 1.5865
## 20 11.7230 nan 0.1000 0.4389
## 40 6.0035 nan 0.1000 -0.0010
## 60 4.8913 nan 0.1000 0.0024
## 80 4.6013 nan 0.1000 -0.0361
## 100 4.4268 nan 0.1000 -0.0313
## 120 4.2823 nan 0.1000 -0.0175
## 140 4.1785 nan 0.1000 -0.0100
## 160 4.0911 nan 0.1000 -0.0423
## 180 4.0082 nan 0.1000 -0.0567
## 200 3.9056 nan 0.1000 -0.0120
## 220 3.8073 nan 0.1000 -0.0044
## 240 3.7222 nan 0.1000 -0.0130
## 260 3.6461 nan 0.1000 -0.0072
## 280 3.5788 nan 0.1000 -0.0369
## 300 3.5061 nan 0.1000 -0.0205
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 54.4538 nan 0.1000 7.4325
## 2 48.5028 nan 0.1000 6.1401
## 3 43.8059 nan 0.1000 4.9498
## 4 39.0279 nan 0.1000 4.3574
## 5 35.4411 nan 0.1000 3.8483
## 6 32.2688 nan 0.1000 3.2946
## 7 29.3795 nan 0.1000 2.8381
## 8 27.0367 nan 0.1000 1.9143
## 9 24.5248 nan 0.1000 2.0962
## 10 22.5315 nan 0.1000 1.9347
## 20 11.8805 nan 0.1000 0.6654
## 40 6.0573 nan 0.1000 0.0025
## 60 4.9301 nan 0.1000 -0.0363
## 80 4.6375 nan 0.1000 0.0014
## 100 4.4643 nan 0.1000 -0.0295
## 120 4.3344 nan 0.1000 -0.0318
## 140 4.2078 nan 0.1000 -0.0197
## 160 4.0812 nan 0.1000 -0.0163
## 180 3.9932 nan 0.1000 -0.0458
## 200 3.8917 nan 0.1000 -0.0093
## 220 3.8157 nan 0.1000 -0.0315
## 240 3.7674 nan 0.1000 -0.0250
## 260 3.6985 nan 0.1000 -0.0056
## 280 3.6336 nan 0.1000 0.0005
## 300 3.5467 nan 0.1000 -0.0326
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 54.8076 nan 0.1000 7.1677
## 2 49.1246 nan 0.1000 5.8667
## 3 44.3561 nan 0.1000 4.3528
## 4 39.8620 nan 0.1000 4.4319
## 5 35.5466 nan 0.1000 4.4480
## 6 32.0148 nan 0.1000 2.8939
## 7 29.2254 nan 0.1000 2.6131
## 8 26.4858 nan 0.1000 2.3813
## 9 24.1384 nan 0.1000 2.1010
## 10 22.1882 nan 0.1000 1.9119
## 20 11.7862 nan 0.1000 0.5451
## 40 6.1845 nan 0.1000 0.0485
## 60 5.1008 nan 0.1000 -0.0325
## 80 4.8234 nan 0.1000 0.0120
## 100 4.5990 nan 0.1000 -0.0033
## 120 4.4453 nan 0.1000 -0.0131
## 140 4.3541 nan 0.1000 -0.0176
## 160 4.2599 nan 0.1000 -0.0030
## 180 4.1840 nan 0.1000 -0.0513
## 200 4.0914 nan 0.1000 -0.0424
## 220 4.0280 nan 0.1000 -0.0460
## 240 3.9639 nan 0.1000 -0.0059
## 260 3.8893 nan 0.1000 -0.0062
## 280 3.8233 nan 0.1000 -0.0216
## 300 3.7812 nan 0.1000 -0.0077
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 52.4696 nan 0.1000 10.0387
## 2 44.0876 nan 0.1000 8.4219
## 3 37.4773 nan 0.1000 5.6257
## 4 32.0671 nan 0.1000 4.8524
## 5 27.5576 nan 0.1000 3.3977
## 6 23.7786 nan 0.1000 3.5954
## 7 20.7378 nan 0.1000 3.0122
## 8 18.2250 nan 0.1000 2.3834
## 9 16.2712 nan 0.1000 1.8746
## 10 14.3526 nan 0.1000 1.6939
## 20 6.2810 nan 0.1000 0.3110
## 40 3.9163 nan 0.1000 -0.0380
## 60 3.2436 nan 0.1000 -0.0344
## 80 2.8314 nan 0.1000 -0.0247
## 100 2.5282 nan 0.1000 -0.0115
## 120 2.2960 nan 0.1000 -0.0135
## 140 2.0580 nan 0.1000 -0.0416
## 160 1.8536 nan 0.1000 -0.0102
## 180 1.7043 nan 0.1000 -0.0245
## 200 1.5893 nan 0.1000 -0.0380
## 220 1.4785 nan 0.1000 -0.0197
## 240 1.3549 nan 0.1000 -0.0197
## 260 1.2608 nan 0.1000 -0.0142
## 280 1.1749 nan 0.1000 -0.0086
## 300 1.1093 nan 0.1000 -0.0141
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 52.3949 nan 0.1000 9.6151
## 2 44.4557 nan 0.1000 7.8735
## 3 37.9398 nan 0.1000 6.1136
## 4 32.4552 nan 0.1000 5.5608
## 5 27.8174 nan 0.1000 4.0227
## 6 24.2423 nan 0.1000 3.7077
## 7 21.3314 nan 0.1000 2.9468
## 8 18.7740 nan 0.1000 2.2866
## 9 16.8861 nan 0.1000 2.0097
## 10 15.2661 nan 0.1000 1.6202
## 20 6.6689 nan 0.1000 0.1888
## 40 4.1151 nan 0.1000 -0.0398
## 60 3.5551 nan 0.1000 -0.0256
## 80 3.2555 nan 0.1000 -0.0548
## 100 2.9841 nan 0.1000 -0.0295
## 120 2.7619 nan 0.1000 -0.0303
## 140 2.5536 nan 0.1000 -0.0316
## 160 2.4049 nan 0.1000 -0.0079
## 180 2.2955 nan 0.1000 -0.0522
## 200 2.1531 nan 0.1000 -0.0189
## 220 2.0583 nan 0.1000 -0.0273
## 240 1.9377 nan 0.1000 -0.0362
## 260 1.8371 nan 0.1000 -0.0132
## 280 1.7450 nan 0.1000 -0.0195
## 300 1.6536 nan 0.1000 -0.0239
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 52.7991 nan 0.1000 9.0010
## 2 44.8187 nan 0.1000 8.0573
## 3 38.1452 nan 0.1000 7.1665
## 4 32.6997 nan 0.1000 5.5486
## 5 28.1284 nan 0.1000 4.4632
## 6 24.3199 nan 0.1000 3.3914
## 7 20.9843 nan 0.1000 3.1358
## 8 18.3428 nan 0.1000 2.4338
## 9 16.4125 nan 0.1000 2.0159
## 10 14.4656 nan 0.1000 1.4699
## 20 6.7510 nan 0.1000 0.3631
## 40 4.2672 nan 0.1000 -0.0018
## 60 3.7883 nan 0.1000 -0.0640
## 80 3.4441 nan 0.1000 -0.0236
## 100 3.2037 nan 0.1000 -0.0341
## 120 3.0127 nan 0.1000 -0.0426
## 140 2.7514 nan 0.1000 -0.0181
## 160 2.6241 nan 0.1000 -0.0175
## 180 2.4976 nan 0.1000 -0.0186
## 200 2.3551 nan 0.1000 -0.0225
## 220 2.2428 nan 0.1000 -0.0187
## 240 2.1528 nan 0.1000 -0.0362
## 260 2.0643 nan 0.1000 -0.0261
## 280 1.9576 nan 0.1000 -0.0155
## 300 1.8813 nan 0.1000 -0.0339
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 52.1119 nan 0.1000 10.1801
## 2 43.9556 nan 0.1000 8.0204
## 3 36.7805 nan 0.1000 6.9267
## 4 31.3349 nan 0.1000 5.4869
## 5 26.8050 nan 0.1000 4.8037
## 6 22.9288 nan 0.1000 3.3492
## 7 19.8097 nan 0.1000 3.2726
## 8 17.2449 nan 0.1000 2.4774
## 9 14.7408 nan 0.1000 2.1059
## 10 12.8890 nan 0.1000 1.6024
## 20 5.2312 nan 0.1000 0.1668
## 40 3.0627 nan 0.1000 -0.0497
## 60 2.4368 nan 0.1000 -0.0341
## 80 2.0174 nan 0.1000 -0.0186
## 100 1.7268 nan 0.1000 -0.0274
## 120 1.5123 nan 0.1000 -0.0335
## 140 1.3280 nan 0.1000 -0.0188
## 160 1.1530 nan 0.1000 -0.0235
## 180 1.0248 nan 0.1000 -0.0078
## 200 0.9313 nan 0.1000 -0.0153
## 220 0.8278 nan 0.1000 -0.0149
## 240 0.7259 nan 0.1000 -0.0149
## 260 0.6464 nan 0.1000 -0.0102
## 280 0.5767 nan 0.1000 -0.0119
## 300 0.5298 nan 0.1000 -0.0115
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 52.0656 nan 0.1000 10.1341
## 2 43.9084 nan 0.1000 7.9801
## 3 37.0762 nan 0.1000 7.0906
## 4 31.0623 nan 0.1000 5.5155
## 5 26.2915 nan 0.1000 3.9790
## 6 22.6846 nan 0.1000 3.2854
## 7 19.5807 nan 0.1000 2.8688
## 8 16.9771 nan 0.1000 2.5670
## 9 14.7056 nan 0.1000 2.1748
## 10 13.0735 nan 0.1000 1.6088
## 20 5.4294 nan 0.1000 0.2670
## 40 3.4078 nan 0.1000 -0.0356
## 60 2.9168 nan 0.1000 -0.0378
## 80 2.5150 nan 0.1000 -0.0234
## 100 2.2520 nan 0.1000 -0.0204
## 120 2.0312 nan 0.1000 -0.0364
## 140 1.7791 nan 0.1000 -0.0303
## 160 1.6362 nan 0.1000 -0.0350
## 180 1.4804 nan 0.1000 -0.0207
## 200 1.3572 nan 0.1000 -0.0084
## 220 1.2256 nan 0.1000 -0.0153
## 240 1.1251 nan 0.1000 -0.0110
## 260 1.0616 nan 0.1000 -0.0150
## 280 0.9769 nan 0.1000 -0.0152
## 300 0.9174 nan 0.1000 -0.0153
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 51.7891 nan 0.1000 9.8440
## 2 43.3241 nan 0.1000 7.7736
## 3 36.4685 nan 0.1000 6.4494
## 4 30.7599 nan 0.1000 5.5524
## 5 26.1759 nan 0.1000 4.5273
## 6 22.3005 nan 0.1000 3.4149
## 7 19.2184 nan 0.1000 2.9957
## 8 16.7347 nan 0.1000 2.5685
## 9 14.5609 nan 0.1000 1.7038
## 10 12.9032 nan 0.1000 1.6362
## 20 5.6709 nan 0.1000 0.2826
## 40 3.8271 nan 0.1000 -0.0562
## 60 3.3528 nan 0.1000 -0.0259
## 80 3.0576 nan 0.1000 -0.0262
## 100 2.7595 nan 0.1000 -0.0389
## 120 2.4658 nan 0.1000 -0.0333
## 140 2.2557 nan 0.1000 -0.0554
## 160 2.0816 nan 0.1000 -0.0224
## 180 1.9530 nan 0.1000 -0.0394
## 200 1.7937 nan 0.1000 -0.0564
## 220 1.6793 nan 0.1000 -0.0134
## 240 1.5793 nan 0.1000 -0.0152
## 260 1.4946 nan 0.1000 -0.0225
## 280 1.4172 nan 0.1000 -0.0181
## 300 1.3446 nan 0.1000 -0.0199
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 60.2761 nan 0.0100 0.8008
## 2 59.5575 nan 0.0100 0.7360
## 3 58.7857 nan 0.0100 0.7765
## 4 58.0223 nan 0.0100 0.6594
## 5 57.2902 nan 0.0100 0.7141
## 6 56.5739 nan 0.0100 0.7383
## 7 55.9285 nan 0.0100 0.6857
## 8 55.2359 nan 0.0100 0.6329
## 9 54.5971 nan 0.0100 0.6439
## 10 53.9664 nan 0.0100 0.6208
## 20 48.0228 nan 0.0100 0.5131
## 40 38.9565 nan 0.0100 0.3685
## 60 32.0781 nan 0.0100 0.2316
## 80 26.9190 nan 0.0100 0.1754
## 100 22.8628 nan 0.0100 0.1511
## 120 19.6574 nan 0.0100 0.1157
## 140 17.1367 nan 0.0100 0.1068
## 160 15.0963 nan 0.0100 0.0742
## 180 13.3989 nan 0.0100 0.0511
## 200 12.0526 nan 0.0100 0.0478
## 220 10.9251 nan 0.0100 0.0443
## 240 9.9798 nan 0.0100 0.0297
## 260 9.1773 nan 0.0100 0.0267
## 280 8.4882 nan 0.0100 0.0215
## 300 7.9452 nan 0.0100 0.0179
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 60.3358 nan 0.0100 0.7410
## 2 59.6381 nan 0.0100 0.7717
## 3 58.9181 nan 0.0100 0.7194
## 4 58.1778 nan 0.0100 0.7511
## 5 57.4200 nan 0.0100 0.6840
## 6 56.7205 nan 0.0100 0.6435
## 7 56.0325 nan 0.0100 0.6156
## 8 55.2890 nan 0.0100 0.6614
## 9 54.6191 nan 0.0100 0.5893
## 10 53.9630 nan 0.0100 0.6470
## 20 48.1116 nan 0.0100 0.5407
## 40 38.8595 nan 0.0100 0.3931
## 60 31.8734 nan 0.0100 0.2925
## 80 26.6940 nan 0.0100 0.2177
## 100 22.7128 nan 0.0100 0.1504
## 120 19.6294 nan 0.0100 0.1181
## 140 17.1552 nan 0.0100 0.1045
## 160 15.1093 nan 0.0100 0.0684
## 180 13.4372 nan 0.0100 0.0561
## 200 12.1154 nan 0.0100 0.0471
## 220 11.0314 nan 0.0100 0.0306
## 240 10.0815 nan 0.0100 0.0335
## 260 9.2700 nan 0.0100 0.0212
## 280 8.5830 nan 0.0100 0.0259
## 300 8.0039 nan 0.0100 0.0283
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 60.3345 nan 0.0100 0.7580
## 2 59.5236 nan 0.0100 0.7380
## 3 58.8020 nan 0.0100 0.7468
## 4 58.0023 nan 0.0100 0.7469
## 5 57.2910 nan 0.0100 0.7150
## 6 56.5618 nan 0.0100 0.6706
## 7 55.8565 nan 0.0100 0.6944
## 8 55.1813 nan 0.0100 0.6619
## 9 54.5096 nan 0.0100 0.6846
## 10 53.8259 nan 0.0100 0.6196
## 20 47.9957 nan 0.0100 0.4979
## 40 39.0757 nan 0.0100 0.3924
## 60 32.0510 nan 0.0100 0.2917
## 80 26.9588 nan 0.0100 0.2144
## 100 22.8597 nan 0.0100 0.1579
## 120 19.6858 nan 0.0100 0.1303
## 140 17.1854 nan 0.0100 0.0814
## 160 15.1114 nan 0.0100 0.0900
## 180 13.4924 nan 0.0100 0.0717
## 200 12.1109 nan 0.0100 0.0486
## 220 10.9870 nan 0.0100 0.0187
## 240 10.0550 nan 0.0100 0.0327
## 260 9.2751 nan 0.0100 0.0275
## 280 8.6431 nan 0.0100 0.0217
## 300 8.0886 nan 0.0100 0.0120
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 60.0433 nan 0.0100 0.9634
## 2 59.1019 nan 0.0100 0.9004
## 3 58.0928 nan 0.0100 0.8345
## 4 57.1744 nan 0.0100 0.8803
## 5 56.1992 nan 0.0100 1.0349
## 6 55.3106 nan 0.0100 0.9595
## 7 54.4148 nan 0.0100 0.9659
## 8 53.5565 nan 0.0100 0.8706
## 9 52.7349 nan 0.0100 0.8061
## 10 51.9506 nan 0.0100 0.7888
## 20 44.4523 nan 0.0100 0.7300
## 40 32.8263 nan 0.0100 0.4898
## 60 24.7771 nan 0.0100 0.3550
## 80 19.1013 nan 0.0100 0.1695
## 100 15.1295 nan 0.0100 0.1507
## 120 12.2295 nan 0.0100 0.1122
## 140 10.1323 nan 0.0100 0.0880
## 160 8.6122 nan 0.0100 0.0665
## 180 7.4402 nan 0.0100 0.0389
## 200 6.5393 nan 0.0100 0.0328
## 220 5.8724 nan 0.0100 0.0198
## 240 5.3834 nan 0.0100 0.0175
## 260 4.9905 nan 0.0100 0.0093
## 280 4.6939 nan 0.0100 0.0063
## 300 4.4521 nan 0.0100 -0.0038
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 60.0225 nan 0.0100 1.0045
## 2 58.9893 nan 0.0100 1.1062
## 3 58.0600 nan 0.0100 0.8951
## 4 57.1438 nan 0.0100 0.9146
## 5 56.1944 nan 0.0100 0.8960
## 6 55.3194 nan 0.0100 0.9240
## 7 54.4099 nan 0.0100 0.9043
## 8 53.4969 nan 0.0100 0.9374
## 9 52.5711 nan 0.0100 0.9185
## 10 51.7597 nan 0.0100 0.8255
## 20 44.2439 nan 0.0100 0.6944
## 40 32.7609 nan 0.0100 0.4770
## 60 24.6358 nan 0.0100 0.3200
## 80 18.9947 nan 0.0100 0.2315
## 100 15.0428 nan 0.0100 0.1512
## 120 12.2678 nan 0.0100 0.1038
## 140 10.1625 nan 0.0100 0.0823
## 160 8.6497 nan 0.0100 0.0484
## 180 7.5598 nan 0.0100 0.0451
## 200 6.7065 nan 0.0100 0.0309
## 220 6.0656 nan 0.0100 0.0145
## 240 5.5642 nan 0.0100 0.0142
## 260 5.1777 nan 0.0100 0.0095
## 280 4.8908 nan 0.0100 0.0092
## 300 4.6551 nan 0.0100 0.0076
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 60.0794 nan 0.0100 0.9119
## 2 59.0456 nan 0.0100 0.8568
## 3 58.0929 nan 0.0100 0.8442
## 4 57.1425 nan 0.0100 0.8541
## 5 56.1999 nan 0.0100 0.9438
## 6 55.2876 nan 0.0100 0.8320
## 7 54.3969 nan 0.0100 0.7467
## 8 53.5732 nan 0.0100 0.8576
## 9 52.7422 nan 0.0100 0.8188
## 10 51.8967 nan 0.0100 0.8492
## 20 44.3652 nan 0.0100 0.6170
## 40 32.7914 nan 0.0100 0.4969
## 60 24.7314 nan 0.0100 0.2972
## 80 19.1481 nan 0.0100 0.2199
## 100 15.2114 nan 0.0100 0.1497
## 120 12.3881 nan 0.0100 0.1018
## 140 10.3871 nan 0.0100 0.0864
## 160 8.8829 nan 0.0100 0.0540
## 180 7.7649 nan 0.0100 0.0396
## 200 6.9283 nan 0.0100 0.0306
## 220 6.3078 nan 0.0100 0.0191
## 240 5.8241 nan 0.0100 0.0129
## 260 5.4501 nan 0.0100 0.0027
## 280 5.1501 nan 0.0100 0.0063
## 300 4.9434 nan 0.0100 -0.0010
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 60.0306 nan 0.0100 1.0784
## 2 59.0280 nan 0.0100 1.0730
## 3 57.9801 nan 0.0100 1.0094
## 4 56.9847 nan 0.0100 1.0551
## 5 56.0450 nan 0.0100 1.0124
## 6 55.0641 nan 0.0100 1.0696
## 7 54.1206 nan 0.0100 0.9663
## 8 53.2228 nan 0.0100 0.9031
## 9 52.2910 nan 0.0100 0.9739
## 10 51.4144 nan 0.0100 0.9420
## 20 43.3036 nan 0.0100 0.6559
## 40 31.2040 nan 0.0100 0.4875
## 60 22.8807 nan 0.0100 0.3367
## 80 17.2145 nan 0.0100 0.2408
## 100 13.3191 nan 0.0100 0.1538
## 120 10.5060 nan 0.0100 0.1223
## 140 8.5624 nan 0.0100 0.0757
## 160 7.1432 nan 0.0100 0.0341
## 180 6.1330 nan 0.0100 0.0330
## 200 5.3965 nan 0.0100 0.0304
## 220 4.8386 nan 0.0100 0.0045
## 240 4.4328 nan 0.0100 0.0084
## 260 4.1189 nan 0.0100 0.0065
## 280 3.8600 nan 0.0100 0.0061
## 300 3.6612 nan 0.0100 0.0019
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 60.0031 nan 0.0100 1.1033
## 2 58.9591 nan 0.0100 1.0304
## 3 57.9819 nan 0.0100 0.9649
## 4 56.9504 nan 0.0100 0.9749
## 5 56.0079 nan 0.0100 0.8591
## 6 55.0450 nan 0.0100 0.9702
## 7 54.1063 nan 0.0100 0.8210
## 8 53.2218 nan 0.0100 0.8920
## 9 52.3569 nan 0.0100 0.8488
## 10 51.4483 nan 0.0100 0.9247
## 20 43.4312 nan 0.0100 0.7435
## 40 31.3662 nan 0.0100 0.4730
## 60 23.2167 nan 0.0100 0.3407
## 80 17.4073 nan 0.0100 0.2523
## 100 13.4865 nan 0.0100 0.1455
## 120 10.7289 nan 0.0100 0.1234
## 140 8.7456 nan 0.0100 0.0729
## 160 7.3458 nan 0.0100 0.0321
## 180 6.3556 nan 0.0100 0.0391
## 200 5.6343 nan 0.0100 0.0225
## 220 5.0878 nan 0.0100 0.0140
## 240 4.6723 nan 0.0100 0.0090
## 260 4.3701 nan 0.0100 0.0021
## 280 4.1307 nan 0.0100 0.0032
## 300 3.9457 nan 0.0100 0.0017
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 60.0601 nan 0.0100 0.9100
## 2 59.0133 nan 0.0100 1.1054
## 3 57.9875 nan 0.0100 0.8578
## 4 57.0170 nan 0.0100 0.9104
## 5 56.1023 nan 0.0100 0.9718
## 6 55.1425 nan 0.0100 0.7635
## 7 54.2256 nan 0.0100 1.0057
## 8 53.3202 nan 0.0100 0.9799
## 9 52.3989 nan 0.0100 0.8590
## 10 51.5373 nan 0.0100 0.7083
## 20 43.6384 nan 0.0100 0.7752
## 40 31.6797 nan 0.0100 0.4688
## 60 23.5365 nan 0.0100 0.3472
## 80 17.8371 nan 0.0100 0.2231
## 100 13.8891 nan 0.0100 0.1541
## 120 11.0696 nan 0.0100 0.1152
## 140 9.0807 nan 0.0100 0.0720
## 160 7.6880 nan 0.0100 0.0510
## 180 6.6640 nan 0.0100 0.0385
## 200 5.9594 nan 0.0100 0.0264
## 220 5.4381 nan 0.0100 0.0192
## 240 5.0597 nan 0.0100 0.0094
## 260 4.7629 nan 0.0100 0.0097
## 280 4.5301 nan 0.0100 0.0022
## 300 4.3435 nan 0.0100 0.0054
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 57.0686 nan 0.0500 3.9884
## 2 53.6808 nan 0.0500 3.2459
## 3 50.5747 nan 0.0500 3.0759
## 4 47.9277 nan 0.0500 2.7992
## 5 45.2158 nan 0.0500 2.5056
## 6 42.6245 nan 0.0500 2.3621
## 7 40.3680 nan 0.0500 2.0140
## 8 38.4157 nan 0.0500 1.9442
## 9 36.4579 nan 0.0500 1.6386
## 10 34.5504 nan 0.0500 1.8207
## 20 21.9010 nan 0.0500 0.6870
## 40 11.8403 nan 0.0500 0.2771
## 60 7.9130 nan 0.0500 0.1087
## 80 6.1085 nan 0.0500 0.0383
## 100 5.2288 nan 0.0500 0.0210
## 120 4.8862 nan 0.0500 -0.0014
## 140 4.6371 nan 0.0500 -0.0030
## 160 4.4916 nan 0.0500 -0.0114
## 180 4.4054 nan 0.0500 -0.0071
## 200 4.3184 nan 0.0500 -0.0503
## 220 4.2497 nan 0.0500 -0.0082
## 240 4.1732 nan 0.0500 -0.0034
## 260 4.1137 nan 0.0500 -0.0073
## 280 4.0693 nan 0.0500 -0.0171
## 300 4.0048 nan 0.0500 -0.0129
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 57.3032 nan 0.0500 3.4137
## 2 53.8451 nan 0.0500 3.3644
## 3 50.8419 nan 0.0500 3.0635
## 4 48.1059 nan 0.0500 2.8637
## 5 45.4951 nan 0.0500 2.6218
## 6 43.2009 nan 0.0500 2.2962
## 7 41.0027 nan 0.0500 2.1720
## 8 38.9278 nan 0.0500 2.3258
## 9 37.0991 nan 0.0500 1.1583
## 10 35.2996 nan 0.0500 1.7821
## 20 22.7003 nan 0.0500 0.7887
## 40 11.8425 nan 0.0500 0.2335
## 60 7.7871 nan 0.0500 0.0997
## 80 6.0970 nan 0.0500 0.0128
## 100 5.2797 nan 0.0500 0.0058
## 120 4.9291 nan 0.0500 0.0113
## 140 4.7586 nan 0.0500 0.0053
## 160 4.6136 nan 0.0500 0.0043
## 180 4.5174 nan 0.0500 -0.0162
## 200 4.4309 nan 0.0500 -0.0119
## 220 4.3630 nan 0.0500 -0.0095
## 240 4.3077 nan 0.0500 -0.0080
## 260 4.2591 nan 0.0500 -0.0239
## 280 4.2061 nan 0.0500 -0.0121
## 300 4.1674 nan 0.0500 -0.0157
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 57.0147 nan 0.0500 3.6156
## 2 53.6643 nan 0.0500 3.2812
## 3 50.5825 nan 0.0500 3.3877
## 4 47.7219 nan 0.0500 2.8477
## 5 44.9022 nan 0.0500 2.2341
## 6 42.7963 nan 0.0500 1.9367
## 7 40.2805 nan 0.0500 2.1741
## 8 38.2368 nan 0.0500 1.8505
## 9 36.2669 nan 0.0500 1.8008
## 10 34.6615 nan 0.0500 1.6843
## 20 22.1453 nan 0.0500 0.9306
## 40 11.9112 nan 0.0500 0.2382
## 60 8.0019 nan 0.0500 0.1019
## 80 6.2892 nan 0.0500 0.0461
## 100 5.4175 nan 0.0500 0.0038
## 120 5.0576 nan 0.0500 -0.0143
## 140 4.8511 nan 0.0500 -0.0234
## 160 4.7268 nan 0.0500 -0.0096
## 180 4.6364 nan 0.0500 -0.0134
## 200 4.5401 nan 0.0500 -0.0005
## 220 4.4543 nan 0.0500 -0.0058
## 240 4.3968 nan 0.0500 -0.0017
## 260 4.3379 nan 0.0500 -0.0115
## 280 4.2758 nan 0.0500 -0.0209
## 300 4.2173 nan 0.0500 -0.0118
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 55.9983 nan 0.0500 4.7744
## 2 51.3313 nan 0.0500 4.1629
## 3 47.2558 nan 0.0500 3.5812
## 4 43.5151 nan 0.0500 3.8992
## 5 40.3991 nan 0.0500 3.1273
## 6 37.2898 nan 0.0500 3.1562
## 7 34.5723 nan 0.0500 2.4273
## 8 32.0515 nan 0.0500 2.3127
## 9 29.7021 nan 0.0500 2.3771
## 10 27.6310 nan 0.0500 2.0703
## 20 14.6352 nan 0.0500 0.9613
## 40 6.4259 nan 0.0500 0.1341
## 60 4.3818 nan 0.0500 0.0446
## 80 3.7663 nan 0.0500 -0.0070
## 100 3.4074 nan 0.0500 -0.0090
## 120 3.1325 nan 0.0500 -0.0145
## 140 2.9468 nan 0.0500 -0.0280
## 160 2.7760 nan 0.0500 -0.0238
## 180 2.6049 nan 0.0500 -0.0169
## 200 2.4491 nan 0.0500 -0.0011
## 220 2.3390 nan 0.0500 -0.0172
## 240 2.2102 nan 0.0500 -0.0116
## 260 2.0966 nan 0.0500 -0.0148
## 280 2.0150 nan 0.0500 -0.0185
## 300 1.9287 nan 0.0500 -0.0253
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 56.1514 nan 0.0500 5.2119
## 2 51.9451 nan 0.0500 4.2393
## 3 47.9775 nan 0.0500 4.0969
## 4 44.1486 nan 0.0500 3.7577
## 5 40.9451 nan 0.0500 3.0020
## 6 37.9026 nan 0.0500 2.8061
## 7 35.1760 nan 0.0500 2.3815
## 8 32.5125 nan 0.0500 2.5488
## 9 30.0636 nan 0.0500 2.3018
## 10 27.9132 nan 0.0500 2.0051
## 20 14.8562 nan 0.0500 0.8340
## 40 6.5387 nan 0.0500 0.1066
## 60 4.5509 nan 0.0500 0.0100
## 80 3.9787 nan 0.0500 -0.0175
## 100 3.6682 nan 0.0500 -0.0467
## 120 3.4519 nan 0.0500 -0.0461
## 140 3.2717 nan 0.0500 -0.0283
## 160 3.0827 nan 0.0500 -0.0097
## 180 2.9268 nan 0.0500 -0.0071
## 200 2.8430 nan 0.0500 -0.0125
## 220 2.7379 nan 0.0500 -0.0200
## 240 2.6309 nan 0.0500 -0.0050
## 260 2.5530 nan 0.0500 -0.0132
## 280 2.4837 nan 0.0500 -0.0016
## 300 2.3895 nan 0.0500 -0.0092
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 56.1639 nan 0.0500 4.8764
## 2 51.9149 nan 0.0500 4.3815
## 3 47.8528 nan 0.0500 3.8368
## 4 43.9098 nan 0.0500 4.2040
## 5 40.3562 nan 0.0500 3.4795
## 6 37.2656 nan 0.0500 2.9575
## 7 34.6563 nan 0.0500 2.8532
## 8 32.1822 nan 0.0500 2.5280
## 9 29.8921 nan 0.0500 2.2462
## 10 27.9332 nan 0.0500 2.0290
## 20 14.9445 nan 0.0500 0.7714
## 40 6.9265 nan 0.0500 0.1457
## 60 4.9536 nan 0.0500 0.0053
## 80 4.3923 nan 0.0500 -0.0079
## 100 4.0726 nan 0.0500 -0.0061
## 120 3.8507 nan 0.0500 -0.0256
## 140 3.6559 nan 0.0500 -0.0085
## 160 3.5282 nan 0.0500 -0.0292
## 180 3.4002 nan 0.0500 -0.0157
## 200 3.2852 nan 0.0500 -0.0093
## 220 3.1708 nan 0.0500 -0.0330
## 240 3.0818 nan 0.0500 -0.0126
## 260 2.9886 nan 0.0500 -0.0144
## 280 2.8937 nan 0.0500 -0.0188
## 300 2.7978 nan 0.0500 -0.0347
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 55.8288 nan 0.0500 4.9513
## 2 50.9527 nan 0.0500 4.3735
## 3 46.5176 nan 0.0500 4.4041
## 4 42.7446 nan 0.0500 3.2939
## 5 39.3044 nan 0.0500 3.2678
## 6 36.3042 nan 0.0500 3.0930
## 7 33.4629 nan 0.0500 3.0656
## 8 31.0432 nan 0.0500 2.3087
## 9 28.7961 nan 0.0500 2.3715
## 10 26.8025 nan 0.0500 2.2347
## 20 13.1550 nan 0.0500 0.9706
## 40 5.3738 nan 0.0500 0.0882
## 60 3.7584 nan 0.0500 0.0220
## 80 3.0958 nan 0.0500 -0.0205
## 100 2.7100 nan 0.0500 -0.0301
## 120 2.4000 nan 0.0500 -0.0071
## 140 2.1467 nan 0.0500 -0.0151
## 160 1.9476 nan 0.0500 -0.0081
## 180 1.7633 nan 0.0500 -0.0275
## 200 1.6450 nan 0.0500 -0.0154
## 220 1.5266 nan 0.0500 -0.0122
## 240 1.4177 nan 0.0500 -0.0093
## 260 1.3207 nan 0.0500 -0.0064
## 280 1.2275 nan 0.0500 -0.0080
## 300 1.1415 nan 0.0500 -0.0056
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 56.1531 nan 0.0500 5.0049
## 2 51.4255 nan 0.0500 4.6784
## 3 47.0509 nan 0.0500 4.1250
## 4 43.2279 nan 0.0500 4.0666
## 5 39.8600 nan 0.0500 3.4924
## 6 36.4985 nan 0.0500 3.2027
## 7 33.6636 nan 0.0500 3.1167
## 8 30.9767 nan 0.0500 2.3953
## 9 28.6653 nan 0.0500 2.0766
## 10 26.4360 nan 0.0500 2.1462
## 20 13.2797 nan 0.0500 0.7163
## 40 5.5330 nan 0.0500 0.0784
## 60 3.9457 nan 0.0500 -0.0058
## 80 3.3950 nan 0.0500 0.0058
## 100 3.0695 nan 0.0500 -0.0232
## 120 2.8488 nan 0.0500 -0.0168
## 140 2.6675 nan 0.0500 -0.0203
## 160 2.4962 nan 0.0500 -0.0142
## 180 2.3408 nan 0.0500 -0.0338
## 200 2.2129 nan 0.0500 -0.0150
## 220 2.0818 nan 0.0500 -0.0183
## 240 1.9452 nan 0.0500 -0.0162
## 260 1.8487 nan 0.0500 -0.0238
## 280 1.7450 nan 0.0500 -0.0091
## 300 1.6702 nan 0.0500 -0.0106
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 55.7093 nan 0.0500 4.3436
## 2 51.0978 nan 0.0500 4.5788
## 3 46.9783 nan 0.0500 3.5963
## 4 43.1662 nan 0.0500 3.5175
## 5 39.5754 nan 0.0500 3.7428
## 6 36.6279 nan 0.0500 3.1279
## 7 33.8223 nan 0.0500 2.7487
## 8 31.1996 nan 0.0500 2.7334
## 9 28.6260 nan 0.0500 2.3199
## 10 26.5668 nan 0.0500 1.9730
## 20 13.3661 nan 0.0500 0.8031
## 40 5.8876 nan 0.0500 0.1000
## 60 4.3764 nan 0.0500 0.0065
## 80 3.8676 nan 0.0500 -0.0103
## 100 3.6180 nan 0.0500 -0.0241
## 120 3.3918 nan 0.0500 -0.0208
## 140 3.1635 nan 0.0500 -0.0167
## 160 2.9961 nan 0.0500 -0.0108
## 180 2.8291 nan 0.0500 -0.0169
## 200 2.6893 nan 0.0500 -0.0197
## 220 2.5717 nan 0.0500 -0.0157
## 240 2.4800 nan 0.0500 -0.0203
## 260 2.3711 nan 0.0500 -0.0177
## 280 2.2768 nan 0.0500 -0.0084
## 300 2.1801 nan 0.0500 -0.0170
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 53.8677 nan 0.1000 7.6444
## 2 47.8801 nan 0.1000 5.9888
## 3 42.7878 nan 0.1000 5.1558
## 4 38.5760 nan 0.1000 4.3065
## 5 34.6497 nan 0.1000 3.5810
## 6 31.1730 nan 0.1000 3.1862
## 7 28.3652 nan 0.1000 2.7487
## 8 25.9222 nan 0.1000 2.6672
## 9 24.0243 nan 0.1000 1.7219
## 10 22.1442 nan 0.1000 1.9251
## 20 11.8167 nan 0.1000 0.4500
## 40 6.0379 nan 0.1000 0.0177
## 60 4.8325 nan 0.1000 -0.0742
## 80 4.4516 nan 0.1000 -0.0102
## 100 4.2707 nan 0.1000 -0.0181
## 120 4.1316 nan 0.1000 -0.0186
## 140 4.0287 nan 0.1000 -0.0507
## 160 3.9035 nan 0.1000 -0.0315
## 180 3.7980 nan 0.1000 -0.0296
## 200 3.7133 nan 0.1000 -0.0530
## 220 3.6525 nan 0.1000 -0.0258
## 240 3.5851 nan 0.1000 -0.0149
## 260 3.5221 nan 0.1000 -0.0199
## 280 3.4688 nan 0.1000 -0.0119
## 300 3.3949 nan 0.1000 -0.0398
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 53.5084 nan 0.1000 7.1754
## 2 47.2144 nan 0.1000 6.3245
## 3 43.0341 nan 0.1000 3.7974
## 4 38.7386 nan 0.1000 4.5051
## 5 34.9836 nan 0.1000 3.8099
## 6 31.5748 nan 0.1000 3.1828
## 7 29.0431 nan 0.1000 2.8182
## 8 26.6336 nan 0.1000 2.0474
## 9 24.3920 nan 0.1000 1.8482
## 10 22.3207 nan 0.1000 1.8764
## 20 11.7955 nan 0.1000 0.4241
## 40 6.0267 nan 0.1000 0.0558
## 60 4.9952 nan 0.1000 0.0030
## 80 4.7756 nan 0.1000 -0.0193
## 100 4.5658 nan 0.1000 -0.0239
## 120 4.4196 nan 0.1000 -0.0105
## 140 4.2895 nan 0.1000 -0.0256
## 160 4.2042 nan 0.1000 -0.0218
## 180 4.1279 nan 0.1000 -0.0395
## 200 4.0555 nan 0.1000 -0.0101
## 220 3.9550 nan 0.1000 -0.0309
## 240 3.9001 nan 0.1000 -0.0213
## 260 3.8208 nan 0.1000 -0.0157
## 280 3.7665 nan 0.1000 -0.0222
## 300 3.7020 nan 0.1000 -0.0197
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 53.0329 nan 0.1000 6.6900
## 2 47.0447 nan 0.1000 5.9615
## 3 41.9518 nan 0.1000 4.8319
## 4 37.8651 nan 0.1000 3.5815
## 5 33.9618 nan 0.1000 3.5096
## 6 31.0610 nan 0.1000 2.9142
## 7 28.3769 nan 0.1000 2.2712
## 8 26.1858 nan 0.1000 1.7849
## 9 23.8608 nan 0.1000 1.9066
## 10 21.9904 nan 0.1000 1.6938
## 20 12.0102 nan 0.1000 0.4134
## 40 6.4074 nan 0.1000 0.0925
## 60 5.3255 nan 0.1000 0.0227
## 80 4.9871 nan 0.1000 -0.0068
## 100 4.7646 nan 0.1000 -0.0191
## 120 4.6076 nan 0.1000 -0.0103
## 140 4.4954 nan 0.1000 -0.0474
## 160 4.4027 nan 0.1000 -0.0107
## 180 4.3389 nan 0.1000 -0.0316
## 200 4.2413 nan 0.1000 -0.0377
## 220 4.1636 nan 0.1000 -0.0502
## 240 4.0818 nan 0.1000 -0.0055
## 260 4.0279 nan 0.1000 -0.0180
## 280 3.9776 nan 0.1000 -0.0232
## 300 3.9315 nan 0.1000 -0.0179
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 51.6074 nan 0.1000 9.4813
## 2 43.7770 nan 0.1000 7.1965
## 3 37.2460 nan 0.1000 6.6488
## 4 32.2083 nan 0.1000 4.7082
## 5 27.8199 nan 0.1000 3.7118
## 6 24.2310 nan 0.1000 3.6556
## 7 21.0037 nan 0.1000 2.6350
## 8 18.3308 nan 0.1000 2.4150
## 9 16.3724 nan 0.1000 1.6787
## 10 14.4304 nan 0.1000 1.7090
## 20 6.3774 nan 0.1000 0.3449
## 40 3.7627 nan 0.1000 -0.0162
## 60 3.1631 nan 0.1000 -0.0807
## 80 2.7504 nan 0.1000 -0.0546
## 100 2.4414 nan 0.1000 -0.0236
## 120 2.2242 nan 0.1000 -0.0403
## 140 2.0076 nan 0.1000 -0.0287
## 160 1.8313 nan 0.1000 -0.0143
## 180 1.6903 nan 0.1000 -0.0091
## 200 1.5727 nan 0.1000 -0.0307
## 220 1.4740 nan 0.1000 -0.0273
## 240 1.3888 nan 0.1000 -0.0209
## 260 1.3076 nan 0.1000 -0.0210
## 280 1.2323 nan 0.1000 -0.0126
## 300 1.1621 nan 0.1000 -0.0230
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 51.5395 nan 0.1000 8.8021
## 2 43.8727 nan 0.1000 7.0956
## 3 37.4693 nan 0.1000 6.4202
## 4 32.2181 nan 0.1000 3.9319
## 5 27.8648 nan 0.1000 4.1166
## 6 23.8674 nan 0.1000 3.5381
## 7 20.7736 nan 0.1000 2.7611
## 8 18.0459 nan 0.1000 2.5673
## 9 15.9883 nan 0.1000 2.1072
## 10 14.3939 nan 0.1000 1.4549
## 20 6.4929 nan 0.1000 0.2404
## 40 4.0851 nan 0.1000 -0.0125
## 60 3.5865 nan 0.1000 -0.0031
## 80 3.2412 nan 0.1000 -0.0388
## 100 2.9754 nan 0.1000 -0.0212
## 120 2.7093 nan 0.1000 -0.0204
## 140 2.5365 nan 0.1000 -0.0146
## 160 2.3725 nan 0.1000 -0.0258
## 180 2.2235 nan 0.1000 -0.0434
## 200 2.1015 nan 0.1000 -0.0280
## 220 1.9917 nan 0.1000 -0.0262
## 240 1.8868 nan 0.1000 -0.0275
## 260 1.7774 nan 0.1000 -0.0284
## 280 1.6900 nan 0.1000 -0.0134
## 300 1.6035 nan 0.1000 -0.0230
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 52.0311 nan 0.1000 9.5023
## 2 44.4135 nan 0.1000 7.6719
## 3 38.3621 nan 0.1000 6.1439
## 4 33.0221 nan 0.1000 5.6061
## 5 28.9102 nan 0.1000 4.2926
## 6 24.8364 nan 0.1000 4.2160
## 7 21.6394 nan 0.1000 3.0735
## 8 19.2988 nan 0.1000 2.5517
## 9 17.1599 nan 0.1000 2.1506
## 10 15.3016 nan 0.1000 1.5437
## 20 6.8800 nan 0.1000 0.2779
## 40 4.3824 nan 0.1000 0.0188
## 60 3.7977 nan 0.1000 -0.0475
## 80 3.4524 nan 0.1000 -0.0375
## 100 3.2551 nan 0.1000 -0.0109
## 120 3.0557 nan 0.1000 -0.0303
## 140 2.8644 nan 0.1000 -0.0209
## 160 2.6966 nan 0.1000 -0.0299
## 180 2.5677 nan 0.1000 -0.0218
## 200 2.4332 nan 0.1000 -0.0197
## 220 2.3221 nan 0.1000 -0.0402
## 240 2.2108 nan 0.1000 -0.0508
## 260 2.1140 nan 0.1000 -0.0159
## 280 2.0249 nan 0.1000 -0.0160
## 300 1.9395 nan 0.1000 -0.0182
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 50.9306 nan 0.1000 10.1220
## 2 42.4697 nan 0.1000 7.8818
## 3 35.5855 nan 0.1000 6.9267
## 4 30.2626 nan 0.1000 4.8325
## 5 25.4988 nan 0.1000 3.9239
## 6 21.7846 nan 0.1000 3.7323
## 7 19.1754 nan 0.1000 2.8123
## 8 16.5570 nan 0.1000 2.3310
## 9 14.4382 nan 0.1000 2.0743
## 10 12.6717 nan 0.1000 1.6600
## 20 5.2304 nan 0.1000 0.2012
## 40 3.1735 nan 0.1000 0.0042
## 60 2.4921 nan 0.1000 -0.0195
## 80 2.0801 nan 0.1000 -0.0476
## 100 1.7569 nan 0.1000 -0.0170
## 120 1.5026 nan 0.1000 -0.0407
## 140 1.3099 nan 0.1000 -0.0236
## 160 1.1569 nan 0.1000 -0.0164
## 180 1.0250 nan 0.1000 -0.0138
## 200 0.9268 nan 0.1000 -0.0218
## 220 0.8406 nan 0.1000 -0.0189
## 240 0.7582 nan 0.1000 -0.0075
## 260 0.6847 nan 0.1000 -0.0115
## 280 0.6190 nan 0.1000 -0.0160
## 300 0.5659 nan 0.1000 -0.0145
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 50.7677 nan 0.1000 10.3466
## 2 42.4192 nan 0.1000 7.7894
## 3 35.4452 nan 0.1000 6.0542
## 4 30.0178 nan 0.1000 4.9837
## 5 25.8880 nan 0.1000 3.6263
## 6 22.3799 nan 0.1000 3.0674
## 7 19.0905 nan 0.1000 3.0272
## 8 16.4027 nan 0.1000 2.4685
## 9 14.3910 nan 0.1000 2.2374
## 10 12.7889 nan 0.1000 1.6632
## 20 5.5572 nan 0.1000 0.0503
## 40 3.5114 nan 0.1000 -0.0139
## 60 2.9355 nan 0.1000 -0.0290
## 80 2.5536 nan 0.1000 -0.0541
## 100 2.3081 nan 0.1000 -0.0475
## 120 2.0764 nan 0.1000 -0.0234
## 140 1.9032 nan 0.1000 -0.0274
## 160 1.7320 nan 0.1000 -0.0303
## 180 1.5830 nan 0.1000 -0.0210
## 200 1.4703 nan 0.1000 -0.0157
## 220 1.3467 nan 0.1000 -0.0215
## 240 1.2332 nan 0.1000 -0.0204
## 260 1.1301 nan 0.1000 -0.0344
## 280 1.0400 nan 0.1000 -0.0146
## 300 0.9682 nan 0.1000 -0.0131
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 51.1003 nan 0.1000 9.3546
## 2 42.9178 nan 0.1000 7.4694
## 3 36.3539 nan 0.1000 6.5993
## 4 31.0834 nan 0.1000 5.2886
## 5 26.5855 nan 0.1000 4.1206
## 6 22.5874 nan 0.1000 3.6964
## 7 19.4913 nan 0.1000 3.1826
## 8 16.9955 nan 0.1000 2.6055
## 9 14.9757 nan 0.1000 1.9573
## 10 13.3151 nan 0.1000 1.5012
## 20 5.9095 nan 0.1000 0.2224
## 40 3.9130 nan 0.1000 -0.0003
## 60 3.3758 nan 0.1000 -0.0229
## 80 3.0844 nan 0.1000 -0.0496
## 100 2.8037 nan 0.1000 -0.0346
## 120 2.5693 nan 0.1000 -0.0050
## 140 2.3889 nan 0.1000 -0.0174
## 160 2.2223 nan 0.1000 -0.0345
## 180 2.0664 nan 0.1000 -0.0389
## 200 1.9129 nan 0.1000 -0.0247
## 220 1.7844 nan 0.1000 -0.0349
## 240 1.6862 nan 0.1000 -0.0264
## 260 1.5918 nan 0.1000 -0.0147
## 280 1.4980 nan 0.1000 -0.0341
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 56.3660 nan 0.0500 4.8121
## 2 51.4808 nan 0.0500 4.4977
## 3 47.0687 nan 0.0500 4.8036
## 4 43.1309 nan 0.0500 3.9498
## 5 39.6542 nan 0.0500 3.3338
## 6 36.6243 nan 0.0500 2.9456
## 7 33.6848 nan 0.0500 2.8345
## 8 31.1140 nan 0.0500 2.5885
## 9 28.6350 nan 0.0500 2.2721
## 10 26.4003 nan 0.0500 1.9125
## 20 12.8657 nan 0.0500 0.7933
## 40 5.2493 nan 0.0500 0.1350
## 60 3.5775 nan 0.0500 -0.0106
## 80 2.9925 nan 0.0500 -0.0117
## 100 2.6640 nan 0.0500 -0.0236
## 120 2.4214 nan 0.0500 -0.0268
## 140 2.1867 nan 0.0500 -0.0006
## 160 1.9970 nan 0.0500 -0.0212
## 180 1.8204 nan 0.0500 -0.0191
## 200 1.6800 nan 0.0500 -0.0040
## 220 1.5605 nan 0.0500 -0.0062
## 240 1.4368 nan 0.0500 -0.0133
## 260 1.3442 nan 0.0500 -0.0080
## 280 1.2618 nan 0.0500 -0.0061
## 300 1.1867 nan 0.0500 -0.0132##################################
# Reporting the cross-validation results
# for the GBM model
##################################
GBM_Tune## Stochastic Gradient Boosting
##
## 294 samples
## 5 predictor
##
## No pre-processing
## Resampling: Cross-Validated (10 fold)
## Summary of sample sizes: 266, 264, 265, 264, 265, 266, ...
## Resampling results across tuning parameters:
##
## shrinkage interaction.depth n.minobsinnode n.trees RMSE Rsquared
## 0.01 1 5 100 4.810935 0.8220024
## 0.01 1 5 200 3.591342 0.8649364
## 0.01 1 5 300 2.996879 0.8894673
## 0.01 1 10 100 4.804888 0.8245577
## 0.01 1 10 200 3.594237 0.8642448
## 0.01 1 10 300 2.996239 0.8897373
## 0.01 1 15 100 4.801958 0.8224158
## 0.01 1 15 200 3.580953 0.8641242
## 0.01 1 15 300 2.999924 0.8879375
## 0.01 3 5 100 3.973208 0.8904896
## 0.01 3 5 200 2.764060 0.9112249
## 0.01 3 5 300 2.425116 0.9185948
## 0.01 3 10 100 3.970773 0.8893448
## 0.01 3 10 200 2.771515 0.9103313
## 0.01 3 10 300 2.441187 0.9176932
## 0.01 3 15 100 3.990870 0.8850612
## 0.01 3 15 200 2.824472 0.9047488
## 0.01 3 15 300 2.489460 0.9135700
## 0.01 5 5 100 3.765540 0.9072375
## 0.01 5 5 200 2.618322 0.9166507
## 0.01 5 5 300 2.380487 0.9195383
## 0.01 5 10 100 3.761667 0.9075920
## 0.01 5 10 200 2.610134 0.9173829
## 0.01 5 10 300 2.374509 0.9205702
## 0.01 5 15 100 3.788274 0.9029016
## 0.01 5 15 200 2.658002 0.9129307
## 0.01 5 15 300 2.418481 0.9169741
## 0.05 1 5 100 2.532506 0.9087920
## 0.05 1 5 200 2.439447 0.9148728
## 0.05 1 5 300 2.437366 0.9142320
## 0.05 1 10 100 2.503174 0.9128347
## 0.05 1 10 200 2.378897 0.9209685
## 0.05 1 10 300 2.370010 0.9210478
## 0.05 1 15 100 2.563312 0.9068743
## 0.05 1 15 200 2.434734 0.9152549
## 0.05 1 15 300 2.438963 0.9157699
## 0.05 3 5 100 2.346351 0.9206420
## 0.05 3 5 200 2.345393 0.9199519
## 0.05 3 5 300 2.327349 0.9207466
## 0.05 3 10 100 2.360410 0.9194875
## 0.05 3 10 200 2.372344 0.9180728
## 0.05 3 10 300 2.358254 0.9191602
## 0.05 3 15 100 2.358306 0.9181427
## 0.05 3 15 200 2.357688 0.9187985
## 0.05 3 15 300 2.366932 0.9176364
## 0.05 5 5 100 2.344008 0.9188591
## 0.05 5 5 200 2.333080 0.9188954
## 0.05 5 5 300 2.319410 0.9198973
## 0.05 5 10 100 2.386873 0.9173964
## 0.05 5 10 200 2.385332 0.9171571
## 0.05 5 10 300 2.376674 0.9172431
## 0.05 5 15 100 2.374062 0.9175198
## 0.05 5 15 200 2.358935 0.9191052
## 0.05 5 15 300 2.360735 0.9193228
## 0.10 1 5 100 2.449071 0.9157931
## 0.10 1 5 200 2.452391 0.9146947
## 0.10 1 5 300 2.470079 0.9134424
## 0.10 1 10 100 2.465371 0.9133767
## 0.10 1 10 200 2.463359 0.9139698
## 0.10 1 10 300 2.445996 0.9142583
## 0.10 1 15 100 2.432597 0.9151404
## 0.10 1 15 200 2.455241 0.9150903
## 0.10 1 15 300 2.434320 0.9161904
## 0.10 3 5 100 2.352778 0.9177282
## 0.10 3 5 200 2.335391 0.9186142
## 0.10 3 5 300 2.335459 0.9178562
## 0.10 3 10 100 2.386385 0.9169080
## 0.10 3 10 200 2.348308 0.9189082
## 0.10 3 10 300 2.339177 0.9188534
## 0.10 3 15 100 2.363800 0.9195673
## 0.10 3 15 200 2.380596 0.9181203
## 0.10 3 15 300 2.362293 0.9199136
## 0.10 5 5 100 2.393913 0.9154127
## 0.10 5 5 200 2.391989 0.9153164
## 0.10 5 5 300 2.398330 0.9139535
## 0.10 5 10 100 2.376873 0.9169394
## 0.10 5 10 200 2.361392 0.9175143
## 0.10 5 10 300 2.392849 0.9160077
## 0.10 5 15 100 2.429391 0.9143777
## 0.10 5 15 200 2.434540 0.9141983
## 0.10 5 15 300 2.452960 0.9136757
## MAE
## 3.877301
## 2.888462
## 2.363814
## 3.869919
## 2.880853
## 2.347499
## 3.863080
## 2.868756
## 2.344566
## 3.273956
## 2.172907
## 1.849299
## 3.274361
## 2.175127
## 1.838932
## 3.278500
## 2.205304
## 1.878976
## 3.103002
## 2.055751
## 1.801944
## 3.090197
## 2.035921
## 1.768429
## 3.111034
## 2.050567
## 1.799572
## 1.905685
## 1.804011
## 1.782111
## 1.887731
## 1.766298
## 1.747344
## 1.960129
## 1.813449
## 1.805008
## 1.755183
## 1.746585
## 1.732468
## 1.738475
## 1.730635
## 1.716780
## 1.749207
## 1.731617
## 1.730280
## 1.725602
## 1.714908
## 1.716469
## 1.773769
## 1.756986
## 1.742463
## 1.768412
## 1.744998
## 1.746614
## 1.821177
## 1.802574
## 1.807528
## 1.842272
## 1.826997
## 1.800826
## 1.783575
## 1.795195
## 1.779606
## 1.738886
## 1.740522
## 1.747177
## 1.739180
## 1.719281
## 1.716109
## 1.760685
## 1.758236
## 1.747422
## 1.749146
## 1.762114
## 1.763512
## 1.746935
## 1.736254
## 1.767303
## 1.792245
## 1.787265
## 1.807362
##
## RMSE was used to select the optimal model using the smallest value.
## The final values used for the model were n.trees = 300, interaction.depth =
## 5, shrinkage = 0.05 and n.minobsinnode = 5.GBM_Tune$finalModel## A gradient boosted model with gaussian loss function.
## 300 iterations were performed.
## There were 5 predictors of which 5 had non-zero influence.(GBM_Tune_RMSE <- GBM_Tune$results[GBM_Tune$results$shrinkage==GBM_Tune$bestTune$shrinkage &
GBM_Tune$results$interaction.depth==GBM_Tune$bestTune$interaction.depth &
GBM_Tune$results$n.minobsinnode==GBM_Tune$bestTune$n.minobsinnode &
GBM_Tune$results$n.trees==GBM_Tune$bestTune$n.trees,
c("RMSE")])## [1] 2.31941(GBM_Tune_Rsquared <- GBM_Tune$results[GBM_Tune$results$shrinkage==GBM_Tune$bestTune$shrinkage &
GBM_Tune$results$interaction.depth==GBM_Tune$bestTune$interaction.depth &
GBM_Tune$results$n.minobsinnode==GBM_Tune$bestTune$n.minobsinnode &
GBM_Tune$results$n.trees==GBM_Tune$bestTune$n.trees,
c("Rsquared")])## [1] 0.9198973(GBM_Tune_MAE <- GBM_Tune$results[GBM_Tune$results$shrinkage==GBM_Tune$bestTune$shrinkage &
GBM_Tune$results$interaction.depth==GBM_Tune$bestTune$interaction.depth &
GBM_Tune$results$n.minobsinnode==GBM_Tune$bestTune$n.minobsinnode &
GBM_Tune$results$n.trees==GBM_Tune$bestTune$n.trees,
c("MAE")])## [1] 1.716469##################################
# Identifying and plotting the
# best model predictors
# for the GBM model
##################################
GBM_VarImp <- varImp(GBM_Tune, scale = TRUE)
plot(GBM_VarImp,
scales=list(y=list(cex = .95)),
main="Ranked Variable Importance : GBM",
xlab="Scaled Variable Importance Metrics",
ylab="Predictors",
cex=2,
origin=0,
alpha=0.45)
1.3.6.2 Random Forest (RF)
Code Chunk | Output
##################################
# Defining the model hyperparameter values
# for the RF model
##################################
RF_Grid = data.frame(mtry = c(100, 200, 300, 400, 500,
600, 700, 800, 900, 1000))
##################################
# Running the RF model
# by setting the caret method to 'RF'
##################################
set.seed(12345678)
RF_Tune <- train(x = MD.Model.Predictors,
y = MD$LIFEXP,
method = "rf",
tuneGrid = RF_Grid,
trControl = KFold_Control)
##################################
# Reporting the cross-validation results
# for the RF model
##################################
RF_Tune## Random Forest
##
## 294 samples
## 5 predictor
##
## No pre-processing
## Resampling: Cross-Validated (10 fold)
## Summary of sample sizes: 266, 264, 265, 264, 265, 266, ...
## Resampling results across tuning parameters:
##
## mtry RMSE Rsquared MAE
## 100 2.469134 0.9069495 1.804822
## 200 2.468106 0.9065615 1.798157
## 300 2.467568 0.9067371 1.800105
## 400 2.454183 0.9077840 1.785219
## 500 2.485343 0.9058410 1.814855
## 600 2.489623 0.9053762 1.817485
## 700 2.480923 0.9058562 1.809700
## 800 2.476728 0.9064718 1.806681
## 900 2.476405 0.9065263 1.801839
## 1000 2.492884 0.9053344 1.814941
##
## RMSE was used to select the optimal model using the smallest value.
## The final value used for the model was mtry = 400.RF_Tune$finalModel##
## Call:
## randomForest(x = x, y = y, mtry = param$mtry)
## Type of random forest: regression
## Number of trees: 500
## No. of variables tried at each split: 5
##
## Mean of squared residuals: 6.28444
## % Var explained: 89.77(RF_Tune_RMSE <- RF_Tune$results[RF_Tune$results$mtry==RF_Tune$bestTune$mtry,
c("RMSE")])## [1] 2.454183(RF_Tune_Rsquared <- RF_Tune$results[RF_Tune$results$mtry==RF_Tune$bestTune$mtry,
c("Rsquared")])## [1] 0.907784(RF_Tune_MAE <- RF_Tune$results[RF_Tune$results$mtry==RF_Tune$bestTune$mtry,
c("MAE")])## [1] 1.785219##################################
# Identifying and plotting the
# best model predictors
# for the RF model
##################################
RF_VarImp <- varImp(RF_Tune, scale = TRUE)
plot(RF_VarImp,
scales=list(y=list(cex = .95)),
main="Ranked Variable Importance : RF",
xlab="Scaled Variable Importance Metrics",
ylab="Predictors",
cex=2,
origin=0,
alpha=0.45)
1.3.6.3 Neural Network (NN)
Code Chunk | Output
##################################
# Defining the model hyperparameter values
# for the NN model
##################################
NN_Grid = expand.grid(size = c(2, 5, 10, 15, 20),
decay = c(0, 0.1, 0.001, 0.0001, 0.00001))
##################################
# Running the NN model
# by setting the caret method to 'NN'
##################################
set.seed(12345678)
NN_Tune <- train(x = MD.Model.Predictors,
y = MD$LIFEXP,
method = "nnet",
linout = TRUE,
preProcess = c('center', 'scale'),
maxit = 500,
tuneGrid = NN_Grid,
trControl = KFold_Control)## # weights: 15
## initial value 1406972.747928
## iter 10 value 17306.731946
## final value 16734.547507
## converged
## # weights: 36
## initial value 1386115.389324
## iter 10 value 3747.659411
## iter 20 value 1974.438861
## iter 30 value 1480.043876
## iter 40 value 1203.077370
## iter 50 value 1099.257974
## iter 60 value 1060.936116
## iter 70 value 1040.946828
## iter 80 value 1034.781443
## iter 90 value 1026.625616
## iter 100 value 1006.773023
## iter 110 value 986.220318
## iter 120 value 957.503659
## iter 130 value 945.283708
## iter 140 value 934.378541
## iter 150 value 926.520491
## iter 160 value 924.816890
## iter 170 value 917.440515
## iter 180 value 914.467570
## iter 190 value 911.122985
## iter 200 value 909.759038
## iter 210 value 909.663170
## iter 220 value 909.372418
## iter 230 value 909.342318
## iter 240 value 908.941445
## iter 250 value 907.867688
## iter 260 value 907.334520
## iter 270 value 907.266447
## iter 280 value 907.253068
## iter 290 value 907.074096
## iter 300 value 906.765869
## final value 906.754491
## converged
## # weights: 71
## initial value 1353444.522438
## iter 10 value 1648.263520
## iter 20 value 1204.419231
## iter 30 value 1072.404973
## iter 40 value 996.768999
## iter 50 value 955.077615
## iter 60 value 926.091176
## iter 70 value 889.713680
## iter 80 value 835.505714
## iter 90 value 800.676805
## iter 100 value 777.747877
## iter 110 value 743.926912
## iter 120 value 705.465974
## iter 130 value 659.865568
## iter 140 value 625.813797
## iter 150 value 619.258581
## iter 160 value 616.155254
## iter 170 value 606.049414
## iter 180 value 595.699481
## iter 190 value 588.072845
## iter 200 value 580.056197
## iter 210 value 567.804995
## iter 220 value 562.505124
## iter 230 value 558.441302
## iter 240 value 553.771010
## iter 250 value 547.974068
## iter 260 value 542.919518
## iter 270 value 539.736711
## iter 280 value 535.595847
## iter 290 value 533.118204
## iter 300 value 532.333468
## iter 310 value 531.018371
## iter 320 value 529.518356
## iter 330 value 528.566091
## iter 340 value 527.798319
## iter 350 value 526.952978
## iter 360 value 525.626017
## iter 370 value 524.360393
## iter 380 value 521.255604
## iter 390 value 519.689711
## iter 400 value 518.712474
## iter 410 value 518.294930
## iter 420 value 518.007947
## iter 430 value 517.981217
## iter 440 value 517.980722
## iter 450 value 517.980321
## iter 460 value 517.979203
## iter 470 value 517.977310
## iter 480 value 517.968718
## iter 490 value 517.837477
## iter 500 value 517.662914
## final value 517.662914
## stopped after 500 iterations
## # weights: 106
## initial value 1409418.840789
## iter 10 value 1325.784047
## iter 20 value 1088.694235
## iter 30 value 962.449694
## iter 40 value 847.693836
## iter 50 value 782.145588
## iter 60 value 695.632632
## iter 70 value 627.861612
## iter 80 value 594.493835
## iter 90 value 575.059432
## iter 100 value 561.086218
## iter 110 value 534.329803
## iter 120 value 490.066511
## iter 130 value 465.480794
## iter 140 value 442.739698
## iter 150 value 431.119941
## iter 160 value 423.454706
## iter 170 value 415.513606
## iter 180 value 409.290801
## iter 190 value 404.673111
## iter 200 value 399.794798
## iter 210 value 396.095946
## iter 220 value 394.305084
## iter 230 value 393.367340
## iter 240 value 391.575001
## iter 250 value 388.956156
## iter 260 value 383.932899
## iter 270 value 372.170220
## iter 280 value 364.740221
## iter 290 value 359.509029
## iter 300 value 351.256116
## iter 310 value 341.675288
## iter 320 value 338.290422
## iter 330 value 336.223563
## iter 340 value 334.964544
## iter 350 value 334.068692
## iter 360 value 333.505675
## iter 370 value 332.808529
## iter 380 value 331.425240
## iter 390 value 329.613177
## iter 400 value 328.332686
## iter 410 value 327.775660
## iter 420 value 327.594785
## iter 430 value 327.531307
## iter 440 value 327.529235
## iter 450 value 327.522892
## iter 460 value 327.514081
## iter 470 value 327.500114
## iter 480 value 327.461898
## iter 490 value 327.224713
## iter 500 value 326.843868
## final value 326.843868
## stopped after 500 iterations
## # weights: 141
## initial value 1398973.066738
## iter 10 value 1686.016326
## iter 20 value 1127.060603
## iter 30 value 944.645340
## iter 40 value 822.849266
## iter 50 value 737.427430
## iter 60 value 671.350469
## iter 70 value 629.045609
## iter 80 value 594.349911
## iter 90 value 562.372525
## iter 100 value 515.929303
## iter 110 value 481.253627
## iter 120 value 442.570930
## iter 130 value 413.209126
## iter 140 value 389.418247
## iter 150 value 367.598205
## iter 160 value 352.650564
## iter 170 value 336.207834
## iter 180 value 323.775668
## iter 190 value 316.764159
## iter 200 value 310.562379
## iter 210 value 301.334935
## iter 220 value 294.104293
## iter 230 value 283.069245
## iter 240 value 273.404821
## iter 250 value 267.714611
## iter 260 value 262.482541
## iter 270 value 253.395413
## iter 280 value 247.847948
## iter 290 value 245.496879
## iter 300 value 244.307030
## iter 310 value 242.492200
## iter 320 value 240.667713
## iter 330 value 239.380813
## iter 340 value 237.968113
## iter 350 value 235.637956
## iter 360 value 229.843322
## iter 370 value 226.485616
## iter 380 value 222.461939
## iter 390 value 215.550406
## iter 400 value 211.480761
## iter 410 value 206.621390
## iter 420 value 201.630312
## iter 430 value 198.889249
## iter 440 value 195.740854
## iter 450 value 193.957590
## iter 460 value 192.173457
## iter 470 value 190.926555
## iter 480 value 189.716988
## iter 490 value 187.874512
## iter 500 value 186.725424
## final value 186.725424
## stopped after 500 iterations
## # weights: 15
## initial value 1400381.846014
## iter 10 value 7067.881683
## iter 20 value 6206.286589
## iter 30 value 3467.746758
## iter 40 value 2324.081266
## iter 50 value 2069.308343
## iter 60 value 1971.540882
## iter 70 value 1733.850963
## iter 80 value 1674.525653
## iter 90 value 1668.600766
## iter 100 value 1662.156476
## final value 1662.044228
## converged
## # weights: 36
## initial value 1425263.534021
## iter 10 value 19451.969836
## iter 20 value 9160.229645
## iter 30 value 7600.056376
## iter 40 value 6700.328755
## iter 50 value 4604.195084
## iter 60 value 3041.680951
## iter 70 value 2514.659972
## iter 80 value 2027.325192
## iter 90 value 1727.217162
## iter 100 value 1519.706835
## iter 110 value 1456.669525
## iter 120 value 1425.909883
## iter 130 value 1411.163155
## iter 140 value 1383.288110
## iter 150 value 1332.521492
## iter 160 value 1268.309496
## iter 170 value 1222.208689
## iter 180 value 1210.416153
## iter 190 value 1202.110126
## iter 200 value 1191.508554
## iter 210 value 1188.634218
## iter 220 value 1181.793186
## iter 230 value 1175.078267
## iter 240 value 1172.038537
## iter 250 value 1171.081216
## final value 1171.037905
## converged
## # weights: 71
## initial value 1384131.732456
## iter 10 value 1976.543920
## iter 20 value 1363.628476
## iter 30 value 1218.826059
## iter 40 value 1134.281800
## iter 50 value 1054.321612
## iter 60 value 1005.843954
## iter 70 value 969.627519
## iter 80 value 938.650414
## iter 90 value 921.522617
## iter 100 value 904.542103
## iter 110 value 894.117665
## iter 120 value 884.474109
## iter 130 value 878.770902
## iter 140 value 876.683479
## iter 150 value 872.973336
## iter 160 value 867.828572
## iter 170 value 863.333203
## iter 180 value 861.022376
## iter 190 value 859.544386
## iter 200 value 853.591475
## iter 210 value 850.086686
## iter 220 value 848.678059
## iter 230 value 846.452737
## iter 240 value 845.705613
## iter 250 value 845.518519
## iter 260 value 845.507494
## final value 845.507466
## converged
## # weights: 106
## initial value 1414503.210071
## iter 10 value 1343.433832
## iter 20 value 1146.624886
## iter 30 value 1023.869360
## iter 40 value 953.781776
## iter 50 value 886.929725
## iter 60 value 855.866975
## iter 70 value 828.138589
## iter 80 value 805.855024
## iter 90 value 791.941820
## iter 100 value 777.446265
## iter 110 value 764.281980
## iter 120 value 749.008650
## iter 130 value 736.414466
## iter 140 value 730.142448
## iter 150 value 723.423440
## iter 160 value 716.986154
## iter 170 value 711.967611
## iter 180 value 708.721393
## iter 190 value 701.550418
## iter 200 value 693.816749
## iter 210 value 688.987131
## iter 220 value 686.398192
## iter 230 value 684.483765
## iter 240 value 681.942192
## iter 250 value 680.032327
## iter 260 value 679.198732
## iter 270 value 678.903392
## iter 280 value 678.789425
## iter 290 value 678.684709
## iter 300 value 678.633211
## iter 310 value 678.624871
## iter 320 value 678.624009
## final value 678.623923
## converged
## # weights: 141
## initial value 1366761.165719
## iter 10 value 1673.371539
## iter 20 value 1286.191377
## iter 30 value 1155.802442
## iter 40 value 1068.274149
## iter 50 value 979.125330
## iter 60 value 923.191796
## iter 70 value 877.834055
## iter 80 value 825.655591
## iter 90 value 790.561121
## iter 100 value 763.789031
## iter 110 value 750.172786
## iter 120 value 735.780027
## iter 130 value 721.767504
## iter 140 value 709.501881
## iter 150 value 696.912583
## iter 160 value 681.545364
## iter 170 value 674.312137
## iter 180 value 670.400298
## iter 190 value 666.222116
## iter 200 value 661.160247
## iter 210 value 654.435844
## iter 220 value 647.226444
## iter 230 value 640.727026
## iter 240 value 636.886235
## iter 250 value 631.626632
## iter 260 value 624.508053
## iter 270 value 619.924054
## iter 280 value 617.545892
## iter 290 value 616.439462
## iter 300 value 614.714624
## iter 310 value 611.756548
## iter 320 value 608.852541
## iter 330 value 605.749551
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## iter 350 value 593.004595
## iter 360 value 586.882036
## iter 370 value 583.490620
## iter 380 value 581.778686
## iter 390 value 580.889623
## iter 400 value 580.289483
## iter 410 value 579.851242
## iter 420 value 579.543287
## iter 430 value 579.238153
## iter 440 value 578.992822
## iter 450 value 578.887321
## iter 460 value 578.874498
## iter 470 value 578.872196
## final value 578.871982
## converged
## # weights: 15
## initial value 1419096.180400
## iter 10 value 24243.164713
## iter 20 value 16818.767637
## iter 30 value 16783.728277
## iter 40 value 9259.528519
## iter 50 value 7749.686175
## iter 60 value 5718.652105
## iter 70 value 3114.259466
## iter 80 value 1818.432927
## iter 90 value 1786.490041
## iter 100 value 1774.681323
## iter 110 value 1767.391368
## iter 120 value 1764.334013
## iter 130 value 1761.385886
## final value 1761.294808
## converged
## # weights: 36
## initial value 1429100.960345
## iter 10 value 24465.631986
## iter 20 value 17605.222419
## iter 30 value 10533.261836
## iter 40 value 4307.695702
## iter 50 value 1662.748767
## iter 60 value 1247.991988
## iter 70 value 1147.915670
## iter 80 value 1125.887587
## iter 90 value 1114.808595
## iter 100 value 1104.140790
## iter 110 value 1087.812745
## iter 120 value 1072.520031
## iter 130 value 1052.024077
## iter 140 value 1040.765059
## iter 150 value 1030.494747
## iter 160 value 1028.937603
## iter 170 value 1021.338011
## iter 180 value 1013.486271
## iter 190 value 1004.193710
## iter 200 value 988.909341
## iter 210 value 973.125634
## iter 220 value 962.630823
## iter 230 value 962.094535
## iter 240 value 960.885653
## iter 250 value 959.483035
## iter 260 value 955.910934
## iter 270 value 954.679085
## iter 280 value 954.069128
## iter 290 value 953.736815
## iter 300 value 953.624310
## iter 310 value 953.482168
## iter 320 value 952.966266
## iter 330 value 952.093411
## iter 340 value 951.415431
## iter 350 value 950.911766
## iter 360 value 950.816826
## iter 370 value 950.769063
## iter 380 value 950.766590
## iter 390 value 950.751871
## iter 400 value 950.740473
## iter 410 value 950.727509
## iter 410 value 950.727508
## iter 410 value 950.727508
## final value 950.727508
## converged
## # weights: 71
## initial value 1401712.653461
## iter 10 value 1738.568841
## iter 20 value 1155.969694
## iter 30 value 1066.155055
## iter 40 value 994.421390
## iter 50 value 926.207042
## iter 60 value 885.460370
## iter 70 value 846.641929
## iter 80 value 785.768292
## iter 90 value 761.296563
## iter 100 value 745.013847
## iter 110 value 735.893526
## iter 120 value 726.662173
## iter 130 value 711.057169
## iter 140 value 696.898191
## iter 150 value 691.769244
## iter 160 value 688.209711
## iter 170 value 684.314315
## iter 180 value 677.004626
## iter 190 value 654.829775
## iter 200 value 620.104902
## iter 210 value 606.666531
## iter 220 value 598.862752
## iter 230 value 593.971127
## iter 240 value 587.916606
## iter 250 value 585.268804
## iter 260 value 582.687583
## iter 270 value 581.012096
## iter 280 value 579.174268
## iter 290 value 576.781503
## iter 300 value 576.419454
## iter 310 value 575.270715
## iter 320 value 572.051387
## iter 330 value 570.105102
## iter 340 value 568.706047
## iter 350 value 562.401978
## iter 360 value 541.358945
## iter 370 value 537.930960
## iter 380 value 534.613389
## iter 390 value 533.733992
## iter 400 value 532.607633
## iter 410 value 532.221327
## iter 420 value 532.085708
## iter 430 value 530.919608
## iter 440 value 524.509179
## iter 450 value 519.374898
## iter 460 value 518.269591
## iter 470 value 517.068506
## iter 480 value 516.304350
## iter 490 value 516.230214
## iter 500 value 516.227264
## final value 516.227264
## stopped after 500 iterations
## # weights: 106
## initial value 1387542.547875
## iter 10 value 2235.721775
## iter 20 value 1219.681810
## iter 30 value 926.131440
## iter 40 value 765.195906
## iter 50 value 690.558284
## iter 60 value 634.275832
## iter 70 value 592.590454
## iter 80 value 547.540247
## iter 90 value 517.199684
## iter 100 value 493.515042
## iter 110 value 479.050776
## iter 120 value 470.329222
## iter 130 value 463.030730
## iter 140 value 449.715226
## iter 150 value 437.567532
## iter 160 value 432.195300
## iter 170 value 427.655493
## iter 180 value 425.090481
## iter 190 value 422.483890
## iter 200 value 419.598959
## iter 210 value 417.697275
## iter 220 value 416.829767
## iter 230 value 416.463387
## iter 240 value 416.068524
## iter 250 value 415.563033
## iter 260 value 414.463444
## iter 270 value 412.521118
## iter 280 value 410.971977
## iter 290 value 409.564001
## iter 300 value 408.638900
## iter 310 value 407.829637
## iter 320 value 407.066443
## iter 330 value 407.027775
## iter 340 value 407.015843
## iter 350 value 407.008033
## iter 360 value 407.006771
## iter 370 value 407.006464
## final value 407.006419
## converged
## # weights: 141
## initial value 1402239.465710
## iter 10 value 3053.206236
## iter 20 value 1196.290006
## iter 30 value 926.935640
## iter 40 value 767.047447
## iter 50 value 682.034414
## iter 60 value 627.714926
## iter 70 value 560.983523
## iter 80 value 508.656911
## iter 90 value 473.707737
## iter 100 value 446.345624
## iter 110 value 426.729946
## iter 120 value 411.078464
## iter 130 value 392.680100
## iter 140 value 370.224893
## iter 150 value 337.476501
## iter 160 value 307.837431
## iter 170 value 299.046501
## iter 180 value 295.389558
## iter 190 value 291.701766
## iter 200 value 285.363260
## iter 210 value 275.058940
## iter 220 value 262.991061
## iter 230 value 256.318150
## iter 240 value 252.021116
## iter 250 value 245.599478
## iter 260 value 243.138813
## iter 270 value 241.202795
## iter 280 value 239.584831
## iter 290 value 238.314803
## iter 300 value 237.212042
## iter 310 value 235.419781
## iter 320 value 233.273965
## iter 330 value 231.424120
## iter 340 value 228.110930
## iter 350 value 224.330065
## iter 360 value 220.771646
## iter 370 value 217.167106
## iter 380 value 212.917724
## iter 390 value 209.748084
## iter 400 value 208.313777
## iter 410 value 205.747497
## iter 420 value 203.215240
## iter 430 value 201.087043
## iter 440 value 200.084978
## iter 450 value 199.753660
## iter 460 value 199.119059
## iter 470 value 198.118019
## iter 480 value 197.129015
## iter 490 value 196.066459
## iter 500 value 195.341673
## final value 195.341673
## stopped after 500 iterations
## # weights: 15
## initial value 1412281.169006
## iter 10 value 6731.743776
## iter 20 value 6379.033364
## iter 30 value 5647.390901
## iter 40 value 5633.300355
## iter 50 value 5577.514875
## iter 60 value 5551.306084
## iter 70 value 4977.434185
## iter 80 value 2776.380282
## iter 90 value 1932.744927
## iter 100 value 1887.601266
## iter 110 value 1847.979838
## iter 120 value 1835.088293
## iter 130 value 1833.105497
## iter 140 value 1828.924685
## iter 150 value 1826.369446
## iter 160 value 1825.722543
## final value 1825.720895
## converged
## # weights: 36
## initial value 1390095.098143
## iter 10 value 5041.909548
## iter 20 value 2366.320459
## iter 30 value 1839.238992
## iter 40 value 1707.588729
## iter 50 value 1679.127363
## iter 60 value 1618.661903
## iter 70 value 1484.753328
## iter 80 value 1392.180254
## iter 90 value 1299.002627
## iter 100 value 1268.296648
## iter 110 value 1244.937003
## iter 120 value 1147.495392
## iter 130 value 1124.845607
## iter 140 value 1120.836930
## iter 150 value 1118.772810
## iter 160 value 1118.163975
## iter 170 value 1117.728992
## iter 180 value 1117.453079
## iter 190 value 1116.382995
## iter 200 value 1116.174988
## iter 210 value 1115.945301
## iter 220 value 1115.726556
## iter 230 value 1115.629768
## iter 240 value 1115.568923
## iter 250 value 1115.566601
## iter 260 value 1115.303561
## iter 270 value 1114.941393
## iter 280 value 1113.969025
## iter 290 value 1112.774834
## iter 300 value 1103.992224
## iter 310 value 1089.450516
## iter 320 value 1083.596173
## iter 330 value 1080.955721
## iter 340 value 1080.601806
## iter 350 value 1077.281110
## iter 360 value 1075.715071
## iter 370 value 1075.612399
## iter 380 value 1074.594064
## iter 390 value 1073.595974
## iter 400 value 1073.333542
## iter 410 value 1072.920142
## iter 420 value 1072.410112
## iter 430 value 1072.237846
## iter 440 value 1072.234172
## iter 450 value 1072.214131
## iter 460 value 1071.926730
## iter 470 value 1071.807091
## iter 480 value 1071.776645
## iter 490 value 1071.749801
## iter 500 value 1071.711436
## final value 1071.711436
## stopped after 500 iterations
## # weights: 71
## initial value 1410171.270412
## iter 10 value 3922.442191
## iter 20 value 1845.385299
## iter 30 value 1367.609950
## iter 40 value 1235.177604
## iter 50 value 1124.674640
## iter 60 value 1036.832182
## iter 70 value 965.602156
## iter 80 value 930.183095
## iter 90 value 903.169432
## iter 100 value 883.843379
## iter 110 value 868.816377
## iter 120 value 858.561122
## iter 130 value 841.498351
## iter 140 value 834.071478
## iter 150 value 832.656036
## iter 160 value 832.305317
## iter 170 value 829.076600
## iter 180 value 825.246079
## iter 190 value 814.529060
## iter 200 value 803.113719
## iter 210 value 793.630889
## iter 220 value 789.577656
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## iter 240 value 784.402387
## iter 250 value 782.373796
## iter 260 value 781.749209
## iter 270 value 781.124557
## iter 280 value 779.764384
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## iter 300 value 775.236538
## iter 310 value 774.331260
## iter 320 value 773.657741
## iter 330 value 773.232376
## iter 340 value 771.625118
## iter 350 value 769.012867
## iter 360 value 765.343257
## iter 370 value 764.266088
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## iter 390 value 763.806990
## iter 400 value 763.574420
## iter 410 value 763.354519
## iter 420 value 763.202166
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## iter 440 value 763.105596
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## iter 470 value 761.849135
## iter 480 value 758.911857
## iter 490 value 756.850933
## iter 500 value 756.137036
## final value 756.137036
## stopped after 500 iterations
## # weights: 106
## initial value 1478360.623413
## iter 10 value 1332.081732
## iter 20 value 1074.661955
## iter 30 value 948.322799
## iter 40 value 847.519714
## iter 50 value 790.092241
## iter 60 value 729.444990
## iter 70 value 663.083422
## iter 80 value 615.765612
## iter 90 value 588.464522
## iter 100 value 574.147784
## iter 110 value 550.918580
## iter 120 value 523.963058
## iter 130 value 500.123274
## iter 140 value 484.232037
## iter 150 value 462.144147
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## iter 180 value 414.627835
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## iter 200 value 391.667554
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## iter 300 value 342.886314
## iter 310 value 336.106750
## iter 320 value 327.551536
## iter 330 value 317.230931
## iter 340 value 309.049753
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## iter 400 value 301.415839
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## iter 420 value 300.972762
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## iter 450 value 300.840149
## iter 460 value 300.819231
## iter 470 value 300.794936
## iter 480 value 300.767890
## iter 490 value 300.731218
## iter 500 value 300.704849
## final value 300.704849
## stopped after 500 iterations
## # weights: 141
## initial value 1485286.129457
## iter 10 value 1403.251952
## iter 20 value 1034.048557
## iter 30 value 906.531608
## iter 40 value 828.173311
## iter 50 value 753.118025
## iter 60 value 689.152492
## iter 70 value 646.366669
## iter 80 value 602.039234
## iter 90 value 559.905052
## iter 100 value 511.450492
## iter 110 value 464.840846
## iter 120 value 439.666439
## iter 130 value 417.329673
## iter 140 value 397.630989
## iter 150 value 388.131968
## iter 160 value 376.564218
## iter 170 value 367.829786
## iter 180 value 356.070019
## iter 190 value 343.605139
## iter 200 value 329.143077
## iter 210 value 317.602004
## iter 220 value 310.053088
## iter 230 value 300.796864
## iter 240 value 294.137987
## iter 250 value 282.768872
## iter 260 value 277.202262
## iter 270 value 273.950637
## iter 280 value 266.753687
## iter 290 value 261.536814
## iter 300 value 259.928962
## iter 310 value 257.704416
## iter 320 value 255.228433
## iter 330 value 252.829330
## iter 340 value 249.950407
## iter 350 value 244.149118
## iter 360 value 238.863935
## iter 370 value 234.341308
## iter 380 value 227.984255
## iter 390 value 219.850093
## iter 400 value 212.599590
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## iter 420 value 203.982743
## iter 430 value 199.481753
## iter 440 value 196.502434
## iter 450 value 195.118177
## iter 460 value 194.367560
## iter 470 value 193.021333
## iter 480 value 191.509376
## iter 490 value 191.246782
## iter 500 value 190.922837
## final value 190.922837
## stopped after 500 iterations
## # weights: 15
## initial value 1393833.087880
## iter 10 value 9255.145350
## iter 20 value 6964.060991
## iter 30 value 5154.555610
## iter 40 value 4096.000374
## iter 50 value 3383.560369
## iter 60 value 2212.645230
## iter 70 value 1630.596245
## iter 80 value 1483.702580
## iter 90 value 1262.396080
## iter 100 value 1195.805107
## iter 110 value 1191.271052
## iter 120 value 1175.860179
## iter 130 value 1169.139250
## iter 140 value 1168.833853
## iter 150 value 1164.432615
## iter 160 value 1162.360653
## iter 170 value 1162.193309
## iter 180 value 1161.026879
## iter 190 value 1159.804129
## iter 200 value 1159.615471
## iter 210 value 1159.216867
## iter 220 value 1158.786125
## iter 230 value 1158.722283
## iter 240 value 1158.393469
## iter 250 value 1158.254385
## iter 260 value 1158.118151
## iter 270 value 1158.025459
## iter 280 value 1157.880653
## iter 290 value 1157.720970
## iter 300 value 1157.718826
## final value 1157.718716
## converged
## # weights: 36
## initial value 1436950.324028
## iter 10 value 37696.048330
## iter 20 value 22008.779316
## iter 30 value 7376.174966
## iter 40 value 4050.017833
## iter 50 value 2071.501161
## iter 60 value 1723.824610
## iter 70 value 1500.510096
## iter 80 value 1451.071267
## iter 90 value 1386.689232
## iter 100 value 1237.043663
## iter 110 value 1136.803726
## iter 120 value 1118.050103
## iter 130 value 1103.988324
## iter 140 value 1083.786702
## iter 150 value 1065.434774
## iter 160 value 1061.956556
## iter 170 value 1054.231372
## iter 180 value 1046.067425
## iter 190 value 1043.519932
## iter 200 value 1039.888204
## iter 210 value 1037.693830
## iter 220 value 1034.989921
## iter 230 value 1034.661807
## iter 240 value 1034.310702
## iter 250 value 1025.715255
## iter 260 value 1018.731726
## iter 270 value 1015.602075
## iter 280 value 1014.959221
## iter 290 value 1014.306198
## iter 300 value 1014.218162
## iter 310 value 1014.125936
## iter 320 value 1013.614775
## iter 330 value 1013.410692
## iter 340 value 1013.092562
## iter 350 value 1012.915281
## iter 360 value 1012.844053
## iter 370 value 1012.764815
## iter 380 value 1012.755096
## iter 390 value 1012.667675
## iter 400 value 1012.645304
## iter 410 value 1012.513499
## iter 420 value 1012.343160
## iter 430 value 1012.197155
## iter 440 value 1012.081758
## iter 450 value 1012.059322
## iter 460 value 1011.995206
## iter 470 value 1011.634320
## iter 480 value 1011.560636
## iter 490 value 1011.486943
## iter 500 value 1011.448402
## final value 1011.448402
## stopped after 500 iterations
## # weights: 71
## initial value 1373795.183354
## iter 10 value 1467.671411
## iter 20 value 1190.361563
## iter 30 value 1058.369983
## iter 40 value 973.855350
## iter 50 value 931.853458
## iter 60 value 910.870881
## iter 70 value 886.244227
## iter 80 value 867.873660
## iter 90 value 851.005555
## iter 100 value 817.233794
## iter 110 value 772.686890
## iter 120 value 749.218785
## iter 130 value 735.352059
## iter 140 value 716.572771
## iter 150 value 699.188656
## iter 160 value 690.381305
## iter 170 value 678.435008
## iter 180 value 660.602861
## iter 190 value 635.974079
## iter 200 value 613.493874
## iter 210 value 602.539371
## iter 220 value 599.064054
## iter 230 value 598.211614
## iter 240 value 596.578873
## iter 250 value 594.386557
## iter 260 value 590.606488
## iter 270 value 589.295301
## iter 280 value 589.041488
## iter 290 value 584.391163
## iter 300 value 578.015877
## iter 310 value 572.985175
## iter 320 value 570.728695
## iter 330 value 570.215283
## iter 340 value 567.625708
## iter 350 value 563.061997
## iter 360 value 560.135472
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## iter 380 value 559.979215
## iter 390 value 559.412550
## iter 400 value 559.091334
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## iter 420 value 559.062563
## iter 430 value 559.061609
## iter 440 value 559.060387
## iter 450 value 559.059577
## iter 460 value 559.057772
## iter 470 value 559.030101
## iter 480 value 559.010226
## iter 490 value 558.869629
## iter 500 value 558.867047
## final value 558.867047
## stopped after 500 iterations
## # weights: 106
## initial value 1422508.792575
## iter 10 value 1604.229250
## iter 20 value 1144.741412
## iter 30 value 1023.547140
## iter 40 value 847.057535
## iter 50 value 762.110977
## iter 60 value 722.613225
## iter 70 value 659.191233
## iter 80 value 599.539229
## iter 90 value 562.358290
## iter 100 value 530.829342
## iter 110 value 503.809161
## iter 120 value 484.532128
## iter 130 value 472.285318
## iter 140 value 455.175128
## iter 150 value 441.172852
## iter 160 value 428.221210
## iter 170 value 417.075854
## iter 180 value 405.347577
## iter 190 value 387.174953
## iter 200 value 365.246886
## iter 210 value 352.059141
## iter 220 value 345.813112
## iter 230 value 343.433402
## iter 240 value 341.587657
## iter 250 value 337.439209
## iter 260 value 334.009899
## iter 270 value 332.205157
## iter 280 value 328.075796
## iter 290 value 318.789430
## iter 300 value 313.849969
## iter 310 value 310.504834
## iter 320 value 309.254215
## iter 330 value 307.750742
## iter 340 value 307.182611
## iter 350 value 306.800059
## iter 360 value 306.242161
## iter 370 value 305.526966
## iter 380 value 305.273011
## iter 390 value 305.032022
## iter 400 value 304.876989
## iter 410 value 304.776477
## iter 420 value 304.669582
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## iter 440 value 304.571252
## iter 450 value 304.562718
## iter 460 value 304.550529
## iter 470 value 304.538611
## iter 480 value 304.516555
## iter 490 value 304.451126
## iter 500 value 304.374046
## final value 304.374046
## stopped after 500 iterations
## # weights: 141
## initial value 1419651.917360
## iter 10 value 3742.707389
## iter 20 value 1844.516600
## iter 30 value 1233.778202
## iter 40 value 924.647094
## iter 50 value 816.118792
## iter 60 value 745.861086
## iter 70 value 686.694500
## iter 80 value 594.468171
## iter 90 value 549.466363
## iter 100 value 521.653829
## iter 110 value 504.165182
## iter 120 value 493.109285
## iter 130 value 483.802735
## iter 140 value 472.122369
## iter 150 value 455.490005
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## iter 170 value 433.127112
## iter 180 value 425.325851
## iter 190 value 402.123044
## iter 200 value 392.430410
## iter 210 value 387.563750
## iter 220 value 383.510923
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## iter 250 value 373.520346
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## iter 300 value 364.529219
## iter 310 value 364.072646
## iter 320 value 362.820390
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## iter 380 value 338.114852
## iter 390 value 333.439967
## iter 400 value 330.706842
## iter 410 value 328.623617
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## iter 480 value 315.690607
## iter 490 value 314.368311
## iter 500 value 313.486442
## final value 313.486442
## stopped after 500 iterations
## # weights: 15
## initial value 1428285.941801
## iter 10 value 10367.997099
## iter 20 value 5384.233863
## iter 30 value 4733.764398
## iter 40 value 4179.718259
## iter 50 value 3515.064505
## iter 60 value 2490.210590
## iter 70 value 1695.148651
## iter 80 value 1440.748098
## iter 90 value 1439.260211
## iter 100 value 1402.164175
## iter 110 value 1390.301325
## iter 120 value 1388.883047
## iter 130 value 1385.512091
## iter 140 value 1380.777015
## iter 150 value 1380.254933
## iter 160 value 1379.884817
## iter 170 value 1377.159901
## iter 180 value 1376.390297
## iter 190 value 1376.321551
## iter 200 value 1375.136304
## iter 210 value 1374.761578
## iter 220 value 1374.754498
## iter 230 value 1374.669136
## iter 240 value 1373.777081
## iter 250 value 1373.742389
## iter 260 value 1373.737753
## iter 270 value 1373.455007
## iter 280 value 1373.156505
## iter 290 value 1373.152758
## iter 300 value 1373.050846
## iter 310 value 1372.676770
## iter 320 value 1372.667689
## final value 1372.666981
## converged
## # weights: 36
## initial value 1416048.944683
## iter 10 value 33595.315372
## iter 20 value 11697.134805
## iter 30 value 7586.798075
## iter 40 value 4967.783918
## iter 50 value 2609.581764
## iter 60 value 1764.424172
## iter 70 value 1424.076549
## iter 80 value 1416.168105
## iter 90 value 1413.657843
## iter 100 value 1391.616466
## iter 110 value 1385.197236
## iter 120 value 1372.459027
## iter 130 value 1365.952730
## iter 140 value 1359.989422
## iter 150 value 1356.785347
## iter 160 value 1353.201929
## iter 170 value 1349.859029
## iter 180 value 1343.542688
## iter 190 value 1312.963527
## iter 200 value 1284.163019
## iter 210 value 1252.490890
## iter 220 value 1232.840004
## iter 230 value 1164.362629
## iter 240 value 1064.279027
## iter 250 value 1002.627750
## iter 260 value 990.943353
## iter 270 value 988.137871
## iter 280 value 986.478458
## iter 290 value 985.087208
## iter 300 value 979.935588
## iter 310 value 975.781223
## iter 320 value 971.956924
## iter 330 value 968.012112
## iter 340 value 966.202190
## iter 350 value 965.728155
## iter 360 value 965.693988
## iter 370 value 965.497300
## iter 380 value 965.373751
## iter 390 value 965.273104
## iter 400 value 965.135688
## iter 410 value 964.956750
## iter 420 value 964.855489
## iter 430 value 964.810530
## iter 440 value 964.333657
## iter 450 value 961.911040
## iter 460 value 961.858375
## iter 470 value 961.402432
## iter 480 value 960.952871
## iter 490 value 960.900232
## iter 500 value 960.884811
## final value 960.884811
## stopped after 500 iterations
## # weights: 71
## initial value 1423448.721199
## iter 10 value 3356.214228
## iter 20 value 1436.194580
## iter 30 value 1115.659147
## iter 40 value 993.275726
## iter 50 value 896.233405
## iter 60 value 854.386689
## iter 70 value 789.048796
## iter 80 value 715.556647
## iter 90 value 677.847337
## iter 100 value 662.108900
## iter 110 value 648.741383
## iter 120 value 635.352515
## iter 130 value 627.995084
## iter 140 value 624.663383
## iter 150 value 622.988015
## iter 160 value 622.451509
## iter 170 value 621.889932
## iter 180 value 620.366400
## iter 190 value 618.109861
## iter 200 value 608.176236
## iter 210 value 595.586930
## iter 220 value 578.130884
## iter 230 value 570.324218
## iter 240 value 561.531646
## iter 250 value 540.408310
## iter 260 value 534.732373
## iter 270 value 531.233066
## iter 280 value 529.224991
## iter 290 value 527.893352
## iter 300 value 527.519890
## iter 310 value 527.280860
## iter 320 value 526.790157
## iter 330 value 525.949117
## iter 340 value 525.798255
## iter 350 value 525.530853
## iter 360 value 524.204819
## iter 370 value 520.281685
## iter 380 value 518.079267
## iter 390 value 517.085005
## iter 400 value 517.069058
## iter 410 value 517.057983
## iter 420 value 517.049201
## iter 430 value 516.946065
## iter 440 value 516.898517
## final value 516.898413
## converged
## # weights: 106
## initial value 1431069.838444
## iter 10 value 1605.362992
## iter 20 value 1038.472955
## iter 30 value 889.850918
## iter 40 value 810.627389
## iter 50 value 765.594627
## iter 60 value 731.579156
## iter 70 value 677.401983
## iter 80 value 642.761799
## iter 90 value 610.444947
## iter 100 value 598.230680
## iter 110 value 586.031659
## iter 120 value 552.625311
## iter 130 value 525.534414
## iter 140 value 505.930038
## iter 150 value 484.843288
## iter 160 value 470.944617
## iter 170 value 457.764296
## iter 180 value 444.370626
## iter 190 value 427.927605
## iter 200 value 411.586044
## iter 210 value 402.896423
## iter 220 value 400.061325
## iter 230 value 398.425777
## iter 240 value 396.308668
## iter 250 value 393.793231
## iter 260 value 391.135145
## iter 270 value 387.873003
## iter 280 value 385.137908
## iter 290 value 379.031102
## iter 300 value 368.587815
## iter 310 value 360.802257
## iter 320 value 356.824018
## iter 330 value 353.533179
## iter 340 value 348.740488
## iter 350 value 342.562836
## iter 360 value 333.505736
## iter 370 value 324.350102
## iter 380 value 320.586783
## iter 390 value 318.125385
## iter 400 value 315.847213
## iter 410 value 314.314988
## iter 420 value 312.608369
## iter 430 value 312.000039
## iter 440 value 311.964930
## iter 450 value 311.896058
## iter 460 value 311.781143
## iter 470 value 311.598088
## iter 480 value 311.019901
## iter 490 value 310.304530
## iter 500 value 308.603216
## final value 308.603216
## stopped after 500 iterations
## # weights: 141
## initial value 1415086.305566
## iter 10 value 1539.025169
## iter 20 value 1073.999351
## iter 30 value 895.746897
## iter 40 value 802.385289
## iter 50 value 714.426882
## iter 60 value 643.529536
## iter 70 value 585.430611
## iter 80 value 547.714703
## iter 90 value 517.931982
## iter 100 value 493.573022
## iter 110 value 473.072988
## iter 120 value 456.125464
## iter 130 value 436.941000
## iter 140 value 411.679932
## iter 150 value 391.803946
## iter 160 value 369.232702
## iter 170 value 348.776458
## iter 180 value 333.787354
## iter 190 value 323.835368
## iter 200 value 312.685920
## iter 210 value 303.662047
## iter 220 value 296.109205
## iter 230 value 290.121973
## iter 240 value 285.850570
## iter 250 value 282.538825
## iter 260 value 280.079597
## iter 270 value 276.903926
## iter 280 value 274.047946
## iter 290 value 272.483554
## iter 300 value 271.585217
## iter 310 value 269.184889
## iter 320 value 266.877628
## iter 330 value 263.078652
## iter 340 value 260.688584
## iter 350 value 258.228147
## iter 360 value 254.034513
## iter 370 value 250.502413
## iter 380 value 247.625926
## iter 390 value 244.496228
## iter 400 value 241.348157
## iter 410 value 239.246278
## iter 420 value 237.087519
## iter 430 value 236.107795
## iter 440 value 234.753297
## iter 450 value 232.660902
## iter 460 value 229.205451
## iter 470 value 226.515100
## iter 480 value 224.522724
## iter 490 value 222.747790
## iter 500 value 221.469696
## final value 221.469696
## stopped after 500 iterations
## # weights: 15
## initial value 1394295.921982
## iter 10 value 15725.178140
## iter 20 value 6950.118426
## iter 30 value 3527.563316
## iter 40 value 2926.561664
## iter 50 value 2082.149682
## iter 60 value 1945.248369
## iter 70 value 1726.936931
## iter 80 value 1626.058896
## iter 90 value 1600.555688
## iter 100 value 1491.584325
## iter 110 value 1430.487849
## iter 120 value 1426.914361
## iter 130 value 1417.844012
## iter 140 value 1417.608921
## final value 1417.605953
## converged
## # weights: 36
## initial value 1347134.598101
## iter 10 value 11028.784269
## iter 20 value 8797.130275
## iter 30 value 6663.104295
## iter 40 value 5565.344710
## iter 50 value 4672.154940
## iter 60 value 2762.743496
## iter 70 value 1907.016013
## iter 80 value 1510.882325
## iter 90 value 1352.205699
## iter 100 value 1242.162793
## iter 110 value 1140.369818
## iter 120 value 1114.828247
## iter 130 value 1093.160072
## iter 140 value 1089.654473
## iter 150 value 1081.250957
## iter 160 value 1074.681742
## iter 170 value 1068.314820
## iter 180 value 1060.152377
## iter 190 value 1057.823395
## iter 200 value 1054.824459
## iter 210 value 1054.120271
## iter 220 value 1053.890553
## iter 230 value 1053.668824
## iter 240 value 1053.626440
## iter 240 value 1053.626435
## iter 240 value 1053.626435
## final value 1053.626435
## converged
## # weights: 71
## initial value 1407159.361970
## iter 10 value 2080.707622
## iter 20 value 1222.959048
## iter 30 value 1139.031292
## iter 40 value 1088.840644
## iter 50 value 1025.596729
## iter 60 value 992.682563
## iter 70 value 957.366108
## iter 80 value 947.218257
## iter 90 value 941.400262
## iter 100 value 932.749781
## iter 110 value 923.724345
## iter 120 value 922.083410
## iter 130 value 920.543013
## iter 140 value 918.743045
## iter 150 value 916.149909
## iter 160 value 912.622761
## iter 170 value 910.312965
## iter 180 value 908.758714
## iter 190 value 907.780680
## iter 200 value 905.829374
## iter 210 value 901.436797
## iter 220 value 899.730391
## iter 230 value 895.945485
## iter 240 value 892.002352
## iter 250 value 887.832319
## iter 260 value 883.059465
## iter 270 value 881.037156
## iter 280 value 880.369143
## iter 290 value 880.030669
## iter 300 value 879.997807
## iter 310 value 879.975875
## iter 320 value 879.961292
## iter 330 value 879.956959
## iter 340 value 879.955164
## final value 879.955030
## converged
## # weights: 106
## initial value 1486846.061428
## iter 10 value 2428.037016
## iter 20 value 1507.325134
## iter 30 value 1305.461535
## iter 40 value 1176.980462
## iter 50 value 1059.562469
## iter 60 value 999.999014
## iter 70 value 963.302151
## iter 80 value 933.666888
## iter 90 value 911.743282
## iter 100 value 894.599268
## iter 110 value 866.311955
## iter 120 value 851.952594
## iter 130 value 846.041335
## iter 140 value 837.459804
## iter 150 value 832.177343
## iter 160 value 828.741008
## iter 170 value 816.753892
## iter 180 value 802.135221
## iter 190 value 794.943347
## iter 200 value 779.809614
## iter 210 value 764.516860
## iter 220 value 749.593789
## iter 230 value 742.031569
## iter 240 value 734.787252
## iter 250 value 731.933168
## iter 260 value 730.611367
## iter 270 value 730.395485
## iter 280 value 730.305644
## iter 290 value 730.219317
## iter 300 value 729.277474
## iter 310 value 717.448151
## iter 320 value 695.859331
## iter 330 value 682.371751
## iter 340 value 675.831175
## iter 350 value 673.195289
## iter 360 value 670.744424
## iter 370 value 668.743232
## iter 380 value 667.929451
## iter 390 value 667.336008
## iter 400 value 666.732642
## iter 410 value 666.524212
## iter 420 value 666.483712
## iter 430 value 666.469360
## iter 440 value 666.467182
## iter 450 value 666.463400
## iter 460 value 666.458956
## iter 470 value 666.424632
## iter 480 value 666.310307
## iter 490 value 666.219338
## iter 500 value 666.188280
## final value 666.188280
## stopped after 500 iterations
## # weights: 141
## initial value 1455583.108312
## iter 10 value 2012.268587
## iter 20 value 1219.817389
## iter 30 value 1036.002579
## iter 40 value 926.844960
## iter 50 value 860.576797
## iter 60 value 799.217322
## iter 70 value 747.281953
## iter 80 value 725.615849
## iter 90 value 713.155563
## iter 100 value 701.083016
## iter 110 value 687.648890
## iter 120 value 677.301906
## iter 130 value 667.633994
## iter 140 value 655.099693
## iter 150 value 633.583338
## iter 160 value 618.453989
## iter 170 value 604.189942
## iter 180 value 589.909041
## iter 190 value 582.114882
## iter 200 value 577.626129
## iter 210 value 572.628799
## iter 220 value 570.313465
## iter 230 value 568.502208
## iter 240 value 567.216887
## iter 250 value 566.071282
## iter 260 value 565.451764
## iter 270 value 565.025617
## iter 280 value 564.733917
## iter 290 value 564.320006
## iter 300 value 563.493828
## iter 310 value 561.614624
## iter 320 value 559.379847
## iter 330 value 557.426177
## iter 340 value 555.927147
## iter 350 value 554.204455
## iter 360 value 551.230347
## iter 370 value 547.665028
## iter 380 value 545.503561
## iter 390 value 544.376962
## iter 400 value 543.851102
## iter 410 value 543.657720
## iter 420 value 543.588437
## iter 430 value 543.557833
## iter 440 value 543.554129
## final value 543.554103
## converged
## # weights: 15
## initial value 1421575.281458
## iter 10 value 23729.334805
## iter 20 value 5960.228677
## iter 30 value 4062.201322
## iter 40 value 3330.827421
## iter 50 value 2292.319589
## iter 60 value 1666.032762
## iter 70 value 1408.885441
## iter 80 value 1361.494750
## iter 90 value 1211.854616
## iter 100 value 1171.792701
## iter 110 value 1169.600978
## iter 120 value 1161.663768
## iter 130 value 1155.590907
## iter 140 value 1154.957299
## iter 150 value 1150.924073
## iter 160 value 1144.617864
## iter 170 value 1143.256593
## iter 180 value 1140.571872
## iter 190 value 1139.691216
## iter 200 value 1139.288432
## iter 210 value 1138.383071
## iter 220 value 1137.841924
## iter 230 value 1137.772143
## final value 1137.772117
## converged
## # weights: 36
## initial value 1381766.120345
## iter 10 value 58309.046647
## iter 20 value 24699.633022
## iter 30 value 4995.195262
## iter 40 value 3354.017372
## iter 50 value 2117.263207
## iter 60 value 1637.241474
## iter 70 value 1628.090930
## iter 80 value 1596.015705
## iter 90 value 1577.414122
## iter 100 value 1544.711459
## iter 110 value 1499.878531
## iter 120 value 1456.151384
## iter 130 value 1412.051719
## iter 140 value 1397.126111
## iter 150 value 1330.213821
## iter 160 value 1238.856298
## iter 170 value 1125.587019
## iter 180 value 1114.735682
## iter 190 value 1111.887815
## iter 200 value 1106.885945
## iter 210 value 1095.507826
## iter 220 value 1053.777491
## iter 230 value 1045.287486
## iter 240 value 1027.479572
## iter 250 value 991.520845
## iter 260 value 982.516983
## iter 270 value 979.950008
## iter 280 value 977.760914
## iter 290 value 976.924834
## iter 300 value 976.847138
## iter 310 value 976.164178
## iter 320 value 975.700124
## iter 330 value 975.273489
## iter 340 value 975.230589
## iter 350 value 975.223833
## final value 975.221511
## converged
## # weights: 71
## initial value 1423539.659459
## iter 10 value 1315.407119
## iter 20 value 1119.872470
## iter 30 value 1040.580066
## iter 40 value 941.801030
## iter 50 value 867.339600
## iter 60 value 797.694983
## iter 70 value 773.720546
## iter 80 value 759.437874
## iter 90 value 744.004492
## iter 100 value 709.861428
## iter 110 value 672.383373
## iter 120 value 652.237052
## iter 130 value 643.158000
## iter 140 value 632.523104
## iter 150 value 628.512726
## iter 160 value 626.310259
## iter 170 value 620.812824
## iter 180 value 617.086361
## iter 190 value 614.386084
## iter 200 value 611.584964
## iter 210 value 608.066207
## iter 220 value 599.674097
## iter 230 value 587.187145
## iter 240 value 579.532699
## iter 250 value 576.012792
## iter 260 value 574.392626
## iter 270 value 570.711436
## iter 280 value 563.364715
## iter 290 value 561.713256
## iter 300 value 561.616435
## iter 310 value 561.470057
## iter 320 value 561.332192
## iter 330 value 561.203690
## iter 340 value 561.056547
## iter 350 value 559.451447
## iter 360 value 547.223586
## iter 370 value 534.521349
## iter 380 value 527.792883
## iter 390 value 526.873359
## iter 400 value 526.542555
## iter 410 value 526.481222
## iter 420 value 526.445360
## iter 430 value 526.436523
## iter 440 value 526.436028
## final value 526.435531
## converged
## # weights: 106
## initial value 1385946.694552
## iter 10 value 1283.998820
## iter 20 value 964.161015
## iter 30 value 864.904072
## iter 40 value 774.064934
## iter 50 value 720.915858
## iter 60 value 682.739131
## iter 70 value 646.348118
## iter 80 value 598.892153
## iter 90 value 569.273105
## iter 100 value 544.770145
## iter 110 value 524.881710
## iter 120 value 504.345923
## iter 130 value 489.064650
## iter 140 value 478.040632
## iter 150 value 465.109342
## iter 160 value 449.925572
## iter 170 value 435.114422
## iter 180 value 421.232060
## iter 190 value 413.849897
## iter 200 value 408.537927
## iter 210 value 404.036039
## iter 220 value 402.072013
## iter 230 value 400.150674
## iter 240 value 397.703607
## iter 250 value 393.044459
## iter 260 value 385.469218
## iter 270 value 372.621015
## iter 280 value 362.681109
## iter 290 value 357.163551
## iter 300 value 352.397977
## iter 310 value 345.030064
## iter 320 value 338.361856
## iter 330 value 334.757936
## iter 340 value 330.877586
## iter 350 value 328.249082
## iter 360 value 326.055109
## iter 370 value 324.699534
## iter 380 value 321.792709
## iter 390 value 315.113484
## iter 400 value 311.605808
## iter 410 value 309.652467
## iter 420 value 305.964070
## iter 430 value 303.907112
## iter 440 value 303.827274
## iter 450 value 303.714376
## iter 460 value 303.517572
## iter 470 value 303.330378
## iter 480 value 302.659422
## iter 490 value 301.533068
## iter 500 value 300.691594
## final value 300.691594
## stopped after 500 iterations
## # weights: 141
## initial value 1384494.474104
## iter 10 value 1652.988125
## iter 20 value 1098.069740
## iter 30 value 909.530477
## iter 40 value 785.522430
## iter 50 value 684.955771
## iter 60 value 613.888064
## iter 70 value 569.088664
## iter 80 value 526.313456
## iter 90 value 503.294104
## iter 100 value 482.748706
## iter 110 value 456.169031
## iter 120 value 431.360694
## iter 130 value 401.683290
## iter 140 value 375.385200
## iter 150 value 355.694948
## iter 160 value 342.281825
## iter 170 value 327.152694
## iter 180 value 317.006709
## iter 190 value 304.036521
## iter 200 value 286.099453
## iter 210 value 273.025805
## iter 220 value 264.809402
## iter 230 value 257.300104
## iter 240 value 252.933665
## iter 250 value 250.299941
## iter 260 value 248.439154
## iter 270 value 247.064123
## iter 280 value 245.373208
## iter 290 value 244.405831
## iter 300 value 243.509478
## iter 310 value 241.511481
## iter 320 value 239.037404
## iter 330 value 234.286909
## iter 340 value 230.584621
## iter 350 value 226.853420
## iter 360 value 223.307497
## iter 370 value 219.637544
## iter 380 value 217.163782
## iter 390 value 214.028496
## iter 400 value 209.624619
## iter 410 value 205.385698
## iter 420 value 203.932566
## iter 430 value 203.280752
## iter 440 value 202.543652
## iter 450 value 201.599221
## iter 460 value 200.066378
## iter 470 value 199.483043
## iter 480 value 199.328437
## iter 490 value 199.250164
## iter 500 value 199.206387
## final value 199.206387
## stopped after 500 iterations
## # weights: 15
## initial value 1389490.039765
## iter 10 value 14324.688282
## iter 20 value 5876.939923
## iter 30 value 4517.780257
## iter 40 value 3059.115888
## iter 50 value 2208.336595
## iter 60 value 1676.424631
## iter 70 value 1592.749486
## iter 80 value 1575.703559
## iter 90 value 1563.508494
## iter 100 value 1558.128344
## iter 110 value 1556.667604
## iter 120 value 1555.986945
## iter 130 value 1554.746909
## iter 140 value 1553.860923
## iter 150 value 1553.856083
## final value 1553.856003
## converged
## # weights: 36
## initial value 1400210.689614
## iter 10 value 11616.858464
## iter 20 value 11380.201817
## iter 30 value 11378.880691
## iter 40 value 11365.829135
## iter 50 value 11297.145617
## iter 60 value 10939.293155
## iter 70 value 9928.053934
## iter 80 value 7624.680954
## iter 90 value 4483.206544
## iter 100 value 2321.367049
## iter 110 value 1733.572420
## iter 120 value 1697.812244
## iter 130 value 1673.129667
## iter 140 value 1633.702204
## iter 150 value 1601.383995
## iter 160 value 1599.592955
## iter 170 value 1592.770646
## iter 180 value 1584.332821
## iter 190 value 1563.978933
## iter 200 value 1466.916266
## iter 210 value 1349.980825
## iter 220 value 1250.204052
## iter 230 value 1174.970879
## iter 240 value 1100.489867
## iter 250 value 998.437449
## iter 260 value 980.413877
## iter 270 value 967.451152
## iter 280 value 955.524788
## iter 290 value 954.054883
## iter 300 value 953.365800
## iter 310 value 949.584130
## iter 320 value 946.209705
## iter 330 value 945.438851
## iter 340 value 944.011329
## iter 350 value 943.122059
## iter 360 value 942.594361
## iter 370 value 942.517112
## iter 380 value 942.304948
## iter 390 value 942.165345
## iter 400 value 941.896572
## iter 410 value 941.795510
## iter 420 value 940.207582
## iter 430 value 934.935149
## iter 440 value 926.876531
## iter 450 value 910.952736
## iter 460 value 909.738978
## iter 470 value 909.576787
## iter 480 value 909.322225
## iter 490 value 908.962058
## iter 500 value 908.960986
## final value 908.960986
## stopped after 500 iterations
## # weights: 71
## initial value 1442226.048993
## iter 10 value 1226.933238
## iter 20 value 1072.830007
## iter 30 value 995.841818
## iter 40 value 932.787010
## iter 50 value 867.421080
## iter 60 value 795.313368
## iter 70 value 739.715865
## iter 80 value 705.987135
## iter 90 value 695.743869
## iter 100 value 688.699032
## iter 110 value 677.839879
## iter 120 value 672.600083
## iter 130 value 668.551755
## iter 140 value 664.636235
## iter 150 value 661.115373
## iter 160 value 657.773261
## iter 170 value 652.088244
## iter 180 value 643.287009
## iter 190 value 635.807584
## iter 200 value 624.115497
## iter 210 value 614.957016
## iter 220 value 610.401177
## iter 230 value 608.533935
## iter 240 value 606.347678
## iter 250 value 601.839140
## iter 260 value 598.744613
## iter 270 value 596.336352
## iter 280 value 594.190099
## iter 290 value 592.567980
## iter 300 value 591.980096
## iter 310 value 591.317805
## iter 320 value 589.723147
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## iter 500 value 526.382879
## final value 526.382879
## stopped after 500 iterations
## # weights: 106
## initial value 1391625.891468
## iter 10 value 1396.734866
## iter 20 value 1094.834159
## iter 30 value 951.118649
## iter 40 value 857.573586
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## final value 301.292486
## stopped after 500 iterations
## # weights: 141
## initial value 1454602.489563
## iter 10 value 1371.006540
## iter 20 value 1064.883584
## iter 30 value 920.586935
## iter 40 value 832.871982
## iter 50 value 755.224488
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## iter 90 value 531.719855
## iter 100 value 459.228664
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## iter 150 value 322.881428
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## iter 170 value 292.996463
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## iter 330 value 208.782022
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## iter 360 value 200.381043
## iter 370 value 197.569194
## iter 380 value 192.738914
## iter 390 value 189.310304
## iter 400 value 187.015938
## iter 410 value 185.022402
## iter 420 value 182.075660
## iter 430 value 178.169691
## iter 440 value 176.305641
## iter 450 value 174.420466
## iter 460 value 172.636490
## iter 470 value 171.318235
## iter 480 value 170.494596
## iter 490 value 169.570920
## iter 500 value 169.021540
## final value 169.021540
## stopped after 500 iterations
## # weights: 15
## initial value 1392144.189633
## iter 10 value 6433.812340
## iter 20 value 2676.120080
## iter 30 value 1811.927804
## iter 40 value 1682.292750
## iter 50 value 1476.332313
## iter 60 value 1315.111942
## iter 70 value 1274.292169
## iter 80 value 1224.564562
## iter 90 value 1160.704046
## iter 100 value 1146.391149
## iter 110 value 1143.077780
## iter 120 value 1138.916403
## iter 130 value 1137.219353
## iter 140 value 1136.078993
## iter 150 value 1133.626263
## iter 160 value 1132.813788
## iter 170 value 1132.331805
## iter 180 value 1132.009354
## iter 190 value 1131.632142
## iter 200 value 1131.453202
## iter 210 value 1131.224373
## final value 1130.792211
## converged
## # weights: 36
## initial value 1359729.007212
## iter 10 value 17691.428547
## iter 20 value 3955.154273
## iter 30 value 2937.702788
## iter 40 value 2024.870173
## iter 50 value 1712.111828
## iter 60 value 1512.661991
## iter 70 value 1393.587728
## iter 80 value 1307.075522
## iter 90 value 1261.002195
## iter 100 value 1154.092973
## iter 110 value 1044.166093
## iter 120 value 1003.069491
## iter 130 value 992.442906
## iter 140 value 980.014704
## iter 150 value 968.787261
## iter 160 value 966.132179
## iter 170 value 962.360079
## iter 180 value 958.594611
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## iter 220 value 941.057242
## iter 230 value 940.661520
## iter 240 value 940.444540
## iter 250 value 938.988496
## iter 260 value 936.402156
## iter 270 value 934.280806
## iter 280 value 933.513869
## iter 290 value 932.854822
## iter 300 value 932.590950
## iter 310 value 932.462597
## iter 320 value 932.319529
## iter 330 value 931.955702
## iter 340 value 930.315471
## iter 350 value 929.575739
## iter 360 value 928.710537
## iter 370 value 928.018720
## iter 380 value 928.004621
## iter 390 value 927.992075
## iter 400 value 927.969310
## iter 410 value 927.289716
## iter 420 value 926.803561
## iter 430 value 926.156505
## iter 440 value 924.715305
## iter 450 value 924.337551
## iter 460 value 923.774407
## iter 470 value 920.706096
## iter 480 value 919.435904
## iter 490 value 917.840575
## iter 500 value 915.410804
## final value 915.410804
## stopped after 500 iterations
## # weights: 71
## initial value 1452113.879510
## iter 10 value 16491.729511
## iter 20 value 12327.550742
## iter 30 value 8290.850180
## iter 40 value 5453.143542
## iter 50 value 3034.536798
## iter 60 value 1477.509172
## iter 70 value 1181.044809
## iter 80 value 1108.703281
## iter 90 value 1071.841831
## iter 100 value 1058.282704
## iter 110 value 1038.735706
## iter 120 value 1027.336030
## iter 130 value 1014.316595
## iter 140 value 1005.312422
## iter 150 value 999.091054
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## iter 180 value 988.559533
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## iter 200 value 977.588167
## iter 210 value 966.704296
## iter 220 value 957.247276
## iter 230 value 949.341368
## iter 240 value 947.068849
## iter 250 value 946.101001
## iter 260 value 942.948448
## iter 270 value 940.805811
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## iter 290 value 940.227769
## iter 300 value 939.835766
## iter 310 value 938.922172
## iter 320 value 938.684092
## iter 330 value 938.289945
## iter 340 value 937.496729
## iter 350 value 936.541986
## iter 360 value 935.175009
## iter 370 value 933.424881
## iter 380 value 933.157244
## iter 390 value 933.056751
## iter 400 value 932.682537
## iter 410 value 932.488279
## iter 420 value 932.179834
## iter 430 value 931.870476
## iter 440 value 931.653296
## iter 450 value 931.492243
## iter 460 value 931.378489
## iter 470 value 931.004088
## iter 480 value 930.747140
## iter 490 value 930.641320
## iter 500 value 930.595132
## final value 930.595132
## stopped after 500 iterations
## # weights: 106
## initial value 1363546.514924
## iter 10 value 1303.333578
## iter 20 value 1003.741511
## iter 30 value 898.282158
## iter 40 value 829.601040
## iter 50 value 773.891295
## iter 60 value 726.391702
## iter 70 value 676.355824
## iter 80 value 640.657594
## iter 90 value 618.890422
## iter 100 value 577.933925
## iter 110 value 539.051787
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## iter 200 value 405.169824
## iter 210 value 394.693626
## iter 220 value 388.628775
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## iter 260 value 362.168470
## iter 270 value 358.706260
## iter 280 value 352.601800
## iter 290 value 348.672214
## iter 300 value 344.775145
## iter 310 value 339.740245
## iter 320 value 337.274193
## iter 330 value 332.083732
## iter 340 value 324.286479
## iter 350 value 320.604535
## iter 360 value 318.145984
## iter 370 value 316.806642
## iter 380 value 315.764522
## iter 390 value 314.830735
## iter 400 value 313.947746
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## iter 420 value 313.004588
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## iter 470 value 312.453986
## iter 480 value 312.331695
## iter 490 value 312.230971
## iter 500 value 312.205064
## final value 312.205064
## stopped after 500 iterations
## # weights: 141
## initial value 1378619.868739
## iter 10 value 1858.928226
## iter 20 value 1062.907500
## iter 30 value 935.561001
## iter 40 value 870.301321
## iter 50 value 759.180047
## iter 60 value 693.564592
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## iter 80 value 586.961667
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## iter 100 value 501.698879
## iter 110 value 468.100924
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## iter 150 value 395.681592
## iter 160 value 383.212258
## iter 170 value 367.002818
## iter 180 value 347.899697
## iter 190 value 334.994893
## iter 200 value 324.765103
## iter 210 value 316.048628
## iter 220 value 306.366314
## iter 230 value 300.964829
## iter 240 value 297.578954
## iter 250 value 293.490389
## iter 260 value 288.899638
## iter 270 value 285.207792
## iter 280 value 281.116034
## iter 290 value 279.382592
## iter 300 value 277.948865
## iter 310 value 276.208802
## iter 320 value 273.220517
## iter 330 value 270.325901
## iter 340 value 265.224281
## iter 350 value 259.221778
## iter 360 value 251.302235
## iter 370 value 243.879029
## iter 380 value 237.093860
## iter 390 value 229.293639
## iter 400 value 217.809341
## iter 410 value 209.193311
## iter 420 value 204.518701
## iter 430 value 200.814327
## iter 440 value 196.174469
## iter 450 value 193.063760
## iter 460 value 190.469498
## iter 470 value 186.892697
## iter 480 value 184.033892
## iter 490 value 182.229653
## iter 500 value 180.302852
## final value 180.302852
## stopped after 500 iterations
## # weights: 15
## initial value 1394897.212124
## iter 10 value 5162.713161
## iter 20 value 5078.609650
## iter 30 value 4801.203746
## iter 40 value 3074.250956
## iter 50 value 2154.882206
## iter 60 value 1924.421827
## iter 70 value 1870.734967
## iter 80 value 1834.852688
## iter 90 value 1823.743128
## iter 100 value 1818.571540
## iter 110 value 1818.536299
## final value 1818.536099
## converged
## # weights: 36
## initial value 1401282.851012
## iter 10 value 299377.549970
## iter 20 value 46719.334411
## iter 30 value 18825.354273
## iter 40 value 3922.768110
## iter 50 value 2157.894096
## iter 60 value 1731.840851
## iter 70 value 1456.286086
## iter 80 value 1338.321858
## iter 90 value 1232.041351
## iter 100 value 1130.596183
## iter 110 value 1019.648902
## iter 120 value 993.516137
## iter 130 value 974.765751
## iter 140 value 953.094140
## iter 150 value 949.117352
## iter 160 value 947.951398
## iter 170 value 942.732152
## iter 180 value 937.182200
## iter 190 value 929.935079
## iter 200 value 925.579572
## iter 210 value 922.699401
## iter 220 value 919.457786
## iter 230 value 919.134128
## iter 240 value 918.217481
## iter 250 value 917.894544
## iter 260 value 917.596083
## iter 270 value 916.856352
## iter 280 value 915.798906
## iter 290 value 914.423154
## iter 300 value 914.096331
## iter 310 value 914.003027
## iter 320 value 913.857292
## iter 330 value 913.802022
## iter 340 value 913.778654
## iter 350 value 913.450678
## iter 360 value 912.620186
## iter 370 value 912.186705
## iter 380 value 912.170843
## iter 390 value 912.001256
## iter 400 value 911.961998
## iter 410 value 911.936799
## iter 420 value 911.924235
## iter 430 value 911.888451
## iter 440 value 911.804263
## iter 450 value 911.797198
## iter 460 value 911.757936
## iter 470 value 911.749491
## iter 480 value 911.745796
## iter 490 value 911.735760
## iter 500 value 911.571557
## final value 911.571557
## stopped after 500 iterations
## # weights: 71
## initial value 1376017.317020
## iter 10 value 3464.612535
## iter 20 value 1751.165603
## iter 30 value 1422.929005
## iter 40 value 1117.303075
## iter 50 value 955.655064
## iter 60 value 888.847305
## iter 70 value 862.120361
## iter 80 value 839.958883
## iter 90 value 827.557725
## iter 100 value 820.683860
## iter 110 value 811.722289
## iter 120 value 804.313757
## iter 130 value 795.707076
## iter 140 value 785.468441
## iter 150 value 781.903630
## iter 160 value 779.800490
## iter 170 value 776.814668
## iter 180 value 770.670435
## iter 190 value 766.385559
## iter 200 value 762.837572
## iter 210 value 749.070654
## iter 220 value 744.160498
## iter 230 value 739.437877
## iter 240 value 736.551619
## iter 250 value 734.489056
## iter 260 value 733.560971
## iter 270 value 733.392458
## iter 280 value 733.145980
## iter 290 value 732.611996
## iter 300 value 732.570223
## iter 310 value 732.457219
## iter 320 value 732.338296
## iter 330 value 732.220644
## iter 340 value 731.745401
## iter 350 value 731.521834
## iter 360 value 731.517942
## iter 370 value 731.506487
## iter 380 value 731.504802
## iter 390 value 731.495499
## iter 400 value 731.461477
## iter 410 value 731.441174
## iter 420 value 731.427676
## iter 430 value 731.421643
## iter 440 value 731.421121
## iter 440 value 731.421116
## iter 440 value 731.421115
## final value 731.421115
## converged
## # weights: 106
## initial value 1407377.914299
## iter 10 value 2074.402667
## iter 20 value 1201.432719
## iter 30 value 1042.422082
## iter 40 value 923.938316
## iter 50 value 831.032753
## iter 60 value 710.205955
## iter 70 value 651.342813
## iter 80 value 615.889866
## iter 90 value 583.348638
## iter 100 value 529.034721
## iter 110 value 504.754408
## iter 120 value 487.981531
## iter 130 value 470.585053
## iter 140 value 460.747388
## iter 150 value 452.717126
## iter 160 value 443.235224
## iter 170 value 435.857171
## iter 180 value 421.020237
## iter 190 value 401.809648
## iter 200 value 393.972519
## iter 210 value 387.049904
## iter 220 value 383.657858
## iter 230 value 381.139890
## iter 240 value 378.160243
## iter 250 value 376.014390
## iter 260 value 372.217909
## iter 270 value 368.573342
## iter 280 value 365.808981
## iter 290 value 355.625908
## iter 300 value 351.126859
## iter 310 value 347.943010
## iter 320 value 343.015882
## iter 330 value 340.542429
## iter 340 value 337.334651
## iter 350 value 330.831954
## iter 360 value 325.399510
## iter 370 value 321.731734
## iter 380 value 319.617890
## iter 390 value 318.451430
## iter 400 value 316.694868
## iter 410 value 315.176868
## iter 420 value 314.780802
## iter 430 value 314.510744
## iter 440 value 314.495566
## iter 450 value 314.479296
## iter 460 value 314.444859
## iter 470 value 314.424583
## iter 480 value 314.406412
## iter 490 value 314.394975
## iter 500 value 314.069708
## final value 314.069708
## stopped after 500 iterations
## # weights: 141
## initial value 1388056.072870
## iter 10 value 1684.295365
## iter 20 value 1153.818933
## iter 30 value 982.515877
## iter 40 value 884.983327
## iter 50 value 791.116418
## iter 60 value 705.268872
## iter 70 value 649.254072
## iter 80 value 579.855527
## iter 90 value 527.283304
## iter 100 value 480.903862
## iter 110 value 446.935913
## iter 120 value 424.665189
## iter 130 value 399.135852
## iter 140 value 370.709539
## iter 150 value 357.444193
## iter 160 value 347.673558
## iter 170 value 335.178396
## iter 180 value 321.796647
## iter 190 value 311.954455
## iter 200 value 300.054588
## iter 210 value 288.263785
## iter 220 value 273.995826
## iter 230 value 266.532930
## iter 240 value 261.576882
## iter 250 value 255.931468
## iter 260 value 247.748924
## iter 270 value 240.945897
## iter 280 value 236.453058
## iter 290 value 234.334535
## iter 300 value 233.439519
## iter 310 value 231.159725
## iter 320 value 228.314014
## iter 330 value 224.902002
## iter 340 value 218.455239
## iter 350 value 207.358814
## iter 360 value 200.892706
## iter 370 value 197.616852
## iter 380 value 194.147778
## iter 390 value 190.508808
## iter 400 value 187.722129
## iter 410 value 183.688059
## iter 420 value 179.321355
## iter 430 value 173.733639
## iter 440 value 168.635558
## iter 450 value 163.646793
## iter 460 value 161.347154
## iter 470 value 159.199635
## iter 480 value 157.566174
## iter 490 value 154.744408
## iter 500 value 153.144467
## final value 153.144467
## stopped after 500 iterations
## # weights: 15
## initial value 1406384.173774
## iter 10 value 13770.535132
## iter 20 value 6442.126009
## iter 30 value 3961.337201
## iter 40 value 3530.230923
## iter 50 value 2129.021526
## iter 60 value 1642.019028
## iter 70 value 1609.446071
## iter 80 value 1560.165248
## iter 90 value 1527.023094
## iter 100 value 1524.871504
## iter 110 value 1522.102226
## iter 120 value 1521.694254
## final value 1521.694064
## converged
## # weights: 36
## initial value 1419345.938229
## iter 10 value 10285.485097
## iter 20 value 2133.955217
## iter 30 value 1585.293843
## iter 40 value 1290.235646
## iter 50 value 1233.916035
## iter 60 value 1208.393003
## iter 70 value 1195.516965
## iter 80 value 1188.829209
## iter 90 value 1185.736841
## iter 100 value 1176.333956
## iter 110 value 1170.663831
## iter 120 value 1161.185878
## iter 130 value 1150.371017
## iter 140 value 1143.791189
## iter 150 value 1141.248770
## iter 160 value 1140.060515
## iter 170 value 1137.697619
## iter 180 value 1136.325388
## iter 190 value 1134.972170
## iter 200 value 1129.388113
## iter 210 value 1127.405256
## iter 220 value 1127.364939
## iter 220 value 1127.364928
## final value 1127.364928
## converged
## # weights: 71
## initial value 1418208.299342
## iter 10 value 1511.385059
## iter 20 value 1201.424527
## iter 30 value 1106.939757
## iter 40 value 1034.609531
## iter 50 value 948.160662
## iter 60 value 890.552004
## iter 70 value 867.892470
## iter 80 value 849.904880
## iter 90 value 832.198211
## iter 100 value 816.405006
## iter 110 value 805.896390
## iter 120 value 797.676841
## iter 130 value 788.006387
## iter 140 value 783.980251
## iter 150 value 780.190004
## iter 160 value 777.333317
## iter 170 value 772.997289
## iter 180 value 769.450843
## iter 190 value 767.194185
## iter 200 value 765.620129
## iter 210 value 764.966629
## iter 220 value 764.893326
## iter 230 value 764.816231
## iter 240 value 763.216966
## iter 250 value 759.392214
## iter 260 value 755.597226
## iter 270 value 752.209103
## iter 280 value 750.418603
## iter 290 value 749.957810
## iter 300 value 749.910971
## iter 310 value 749.792740
## iter 320 value 749.710589
## iter 330 value 749.066642
## iter 340 value 745.329790
## iter 350 value 739.117790
## iter 360 value 738.097433
## iter 370 value 738.085150
## final value 738.085114
## converged
## # weights: 106
## initial value 1449985.119957
## iter 10 value 1758.890706
## iter 20 value 1214.730413
## iter 30 value 1045.211963
## iter 40 value 932.133679
## iter 50 value 886.510521
## iter 60 value 847.062979
## iter 70 value 810.493104
## iter 80 value 784.385695
## iter 90 value 765.576851
## iter 100 value 752.741255
## iter 110 value 738.455595
## iter 120 value 730.039813
## iter 130 value 716.952158
## iter 140 value 706.587225
## iter 150 value 697.176892
## iter 160 value 691.003893
## iter 170 value 686.186771
## iter 180 value 682.886178
## iter 190 value 678.854083
## iter 200 value 676.074763
## iter 210 value 673.910854
## iter 220 value 672.925219
## iter 230 value 670.982874
## iter 240 value 667.475003
## iter 250 value 665.475129
## iter 260 value 663.798610
## iter 270 value 662.945312
## iter 280 value 662.202662
## iter 290 value 659.282755
## iter 300 value 656.427126
## iter 310 value 646.784586
## iter 320 value 634.594268
## iter 330 value 626.846997
## iter 340 value 619.842910
## iter 350 value 617.088115
## iter 360 value 616.137738
## iter 370 value 615.775151
## iter 380 value 615.587091
## iter 390 value 615.289510
## iter 400 value 615.200831
## iter 410 value 615.177809
## iter 420 value 615.175197
## final value 615.174503
## converged
## # weights: 141
## initial value 1442019.296828
## iter 10 value 2135.762394
## iter 20 value 1245.768955
## iter 30 value 1055.972566
## iter 40 value 907.837633
## iter 50 value 813.404619
## iter 60 value 758.620533
## iter 70 value 718.241072
## iter 80 value 681.105110
## iter 90 value 663.018197
## iter 100 value 648.306951
## iter 110 value 636.832177
## iter 120 value 627.259535
## iter 130 value 619.276473
## iter 140 value 611.373841
## iter 150 value 605.894464
## iter 160 value 597.688702
## iter 170 value 593.212221
## iter 180 value 587.419283
## iter 190 value 583.198573
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## iter 410 value 475.273422
## iter 420 value 472.124650
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## iter 460 value 462.960269
## iter 470 value 461.886329
## iter 480 value 461.574195
## iter 490 value 461.484284
## iter 500 value 461.456913
## final value 461.456913
## stopped after 500 iterations
## # weights: 15
## initial value 1406748.916990
## iter 10 value 5344.778159
## iter 20 value 4171.474859
## iter 30 value 3762.020870
## iter 40 value 3591.519595
## iter 50 value 3169.823943
## iter 60 value 2755.674271
## iter 70 value 1931.314589
## iter 80 value 1591.791014
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## iter 170 value 1248.235828
## iter 180 value 1247.843143
## iter 190 value 1247.828647
## iter 200 value 1247.804769
## final value 1247.804612
## converged
## # weights: 36
## initial value 1404654.402350
## iter 10 value 5467.228743
## iter 20 value 3625.834662
## iter 30 value 3079.268039
## iter 40 value 2932.355537
## iter 50 value 2418.294763
## iter 60 value 1667.722069
## iter 70 value 1509.250946
## iter 80 value 1332.558187
## iter 90 value 1286.847206
## iter 100 value 1269.895138
## iter 110 value 1251.510245
## iter 120 value 1244.060223
## iter 130 value 1218.629738
## iter 140 value 1196.282727
## iter 150 value 1149.970287
## iter 160 value 1099.627041
## iter 170 value 1066.795443
## iter 180 value 1016.979808
## iter 190 value 995.532014
## iter 200 value 989.616527
## iter 210 value 988.702919
## iter 220 value 983.951636
## iter 230 value 970.995146
## iter 240 value 963.502618
## iter 250 value 961.800353
## iter 260 value 961.319842
## iter 270 value 961.045437
## iter 280 value 961.036412
## iter 290 value 960.997069
## iter 300 value 960.892077
## iter 310 value 960.795150
## iter 320 value 960.756013
## iter 330 value 960.722899
## iter 340 value 960.566816
## iter 350 value 960.508370
## iter 360 value 960.501237
## iter 370 value 960.387318
## iter 380 value 959.168468
## iter 390 value 936.004814
## iter 400 value 929.486939
## iter 410 value 928.064978
## iter 420 value 926.698322
## iter 430 value 926.637411
## iter 440 value 926.497954
## iter 450 value 925.969762
## iter 460 value 925.702800
## iter 470 value 925.630139
## iter 480 value 925.621276
## final value 925.620160
## converged
## # weights: 71
## initial value 1448594.288173
## iter 10 value 1998.884294
## iter 20 value 1249.210458
## iter 30 value 1039.331207
## iter 40 value 949.907337
## iter 50 value 885.072117
## iter 60 value 836.653176
## iter 70 value 803.250524
## iter 80 value 772.197971
## iter 90 value 715.745609
## iter 100 value 682.632037
## iter 110 value 669.647726
## iter 120 value 658.561556
## iter 130 value 647.574728
## iter 140 value 633.092437
## iter 150 value 629.414717
## iter 160 value 628.385676
## iter 170 value 626.133596
## iter 180 value 619.577671
## iter 190 value 614.019318
## iter 200 value 606.773262
## iter 210 value 591.076212
## iter 220 value 579.598162
## iter 230 value 573.723423
## iter 240 value 565.237458
## iter 250 value 562.430163
## iter 260 value 560.967748
## iter 270 value 560.312162
## iter 280 value 559.792420
## iter 290 value 559.499271
## iter 300 value 559.482463
## iter 310 value 559.434718
## iter 320 value 559.343581
## iter 330 value 559.220237
## iter 340 value 559.174525
## iter 350 value 559.080966
## iter 360 value 559.064228
## iter 370 value 559.060605
## final value 559.060347
## converged
## # weights: 106
## initial value 1371057.175926
## iter 10 value 1603.382123
## iter 20 value 1100.374844
## iter 30 value 925.027665
## iter 40 value 745.631744
## iter 50 value 655.625838
## iter 60 value 589.808411
## iter 70 value 556.493895
## iter 80 value 537.184875
## iter 90 value 522.169353
## iter 100 value 509.229286
## iter 110 value 499.542885
## iter 120 value 492.351116
## iter 130 value 481.882074
## iter 140 value 466.555841
## iter 150 value 452.173222
## iter 160 value 438.563390
## iter 170 value 429.011152
## iter 180 value 423.009685
## iter 190 value 411.752831
## iter 200 value 400.183627
## iter 210 value 393.507508
## iter 220 value 390.993711
## iter 230 value 388.846996
## iter 240 value 384.655022
## iter 250 value 380.523247
## iter 260 value 373.929378
## iter 270 value 366.448851
## iter 280 value 361.663323
## iter 290 value 357.166943
## iter 300 value 350.395028
## iter 310 value 344.855082
## iter 320 value 338.122554
## iter 330 value 331.705616
## iter 340 value 325.879371
## iter 350 value 320.352971
## iter 360 value 316.539782
## iter 370 value 313.477916
## iter 380 value 311.284552
## iter 390 value 309.131812
## iter 400 value 305.835875
## iter 410 value 302.043903
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## iter 460 value 295.993659
## iter 470 value 295.440906
## iter 480 value 294.851147
## iter 490 value 294.474237
## iter 500 value 294.139842
## final value 294.139842
## stopped after 500 iterations
## # weights: 141
## initial value 1433748.111499
## iter 10 value 1716.685914
## iter 20 value 1126.974194
## iter 30 value 848.670411
## iter 40 value 731.991249
## iter 50 value 647.619439
## iter 60 value 554.629487
## iter 70 value 514.718376
## iter 80 value 473.565282
## iter 90 value 445.613081
## iter 100 value 420.690106
## iter 110 value 393.139626
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## iter 130 value 346.903443
## iter 140 value 323.140093
## iter 150 value 300.935461
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## iter 200 value 246.904057
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## iter 310 value 211.321963
## iter 320 value 210.552533
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## iter 360 value 200.154507
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## iter 470 value 176.637584
## iter 480 value 176.057998
## iter 490 value 175.813406
## iter 500 value 175.678030
## final value 175.678030
## stopped after 500 iterations
## # weights: 15
## initial value 1414275.057729
## iter 10 value 15993.717911
## iter 20 value 14452.202363
## iter 30 value 7909.059426
## iter 40 value 4864.348001
## iter 50 value 2074.802229
## iter 60 value 1869.589327
## iter 70 value 1854.122154
## iter 80 value 1773.932000
## iter 90 value 1693.198275
## iter 100 value 1652.898882
## iter 110 value 1646.097513
## iter 120 value 1630.981011
## iter 130 value 1624.764947
## iter 140 value 1619.108699
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## iter 160 value 1614.566990
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## iter 180 value 1612.505240
## iter 190 value 1608.285391
## iter 200 value 1598.591962
## iter 210 value 1504.491139
## iter 220 value 1473.929736
## iter 230 value 1466.841804
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## iter 250 value 1456.437960
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## iter 270 value 1455.189793
## iter 280 value 1455.049900
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## iter 300 value 1454.814431
## iter 310 value 1454.605030
## iter 320 value 1454.583014
## iter 330 value 1454.282042
## iter 340 value 1454.258043
## iter 350 value 1454.252589
## iter 360 value 1454.237810
## iter 370 value 1454.202741
## final value 1454.195031
## converged
## # weights: 36
## initial value 1402841.127600
## iter 10 value 228827.546118
## iter 20 value 10211.092963
## iter 30 value 6251.151183
## iter 40 value 5867.695134
## iter 50 value 5609.435087
## iter 60 value 5548.416626
## iter 70 value 5546.900389
## iter 80 value 5438.673466
## iter 90 value 5074.979683
## iter 100 value 4484.504870
## iter 110 value 4073.955045
## iter 120 value 3408.894762
## iter 130 value 2776.611584
## iter 140 value 2257.032629
## iter 150 value 1869.538054
## iter 160 value 1777.757859
## iter 170 value 1715.453915
## iter 180 value 1664.946109
## iter 190 value 1657.823633
## iter 200 value 1565.906107
## iter 210 value 1523.844259
## iter 220 value 1504.158555
## iter 230 value 1500.596163
## iter 240 value 1494.814806
## iter 250 value 1483.980230
## iter 260 value 1479.722417
## iter 270 value 1470.198098
## iter 280 value 1468.757975
## iter 290 value 1463.498356
## iter 300 value 1461.558397
## iter 310 value 1460.552166
## iter 320 value 1460.280731
## iter 330 value 1459.225552
## iter 340 value 1458.314760
## iter 350 value 1457.799016
## iter 360 value 1437.935802
## iter 370 value 1422.248602
## iter 380 value 1403.731008
## iter 390 value 1382.191780
## iter 400 value 1381.367892
## iter 410 value 1379.709743
## iter 420 value 1376.487023
## iter 430 value 1354.741249
## iter 440 value 1345.648898
## iter 450 value 1345.430448
## iter 460 value 1345.325696
## iter 470 value 1344.149590
## iter 480 value 1343.522160
## iter 490 value 1342.892640
## iter 500 value 1341.942434
## final value 1341.942434
## stopped after 500 iterations
## # weights: 71
## initial value 1449975.565530
## iter 10 value 40444.399758
## iter 20 value 6075.795049
## iter 30 value 3862.815550
## iter 40 value 3083.978612
## iter 50 value 2662.046461
## iter 60 value 2322.254135
## iter 70 value 1954.854662
## iter 80 value 1583.901698
## iter 90 value 1444.266789
## iter 100 value 1398.010418
## iter 110 value 1372.933580
## iter 120 value 1351.217501
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## iter 170 value 1157.246799
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## iter 200 value 996.808407
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## iter 250 value 888.570445
## iter 260 value 882.932774
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## iter 330 value 871.969478
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## iter 470 value 823.829291
## iter 480 value 822.432562
## iter 490 value 821.975579
## iter 500 value 821.735226
## final value 821.735226
## stopped after 500 iterations
## # weights: 106
## initial value 1336980.291867
## iter 10 value 1382.243435
## iter 20 value 1128.860289
## iter 30 value 1021.675856
## iter 40 value 914.316224
## iter 50 value 837.577559
## iter 60 value 763.040455
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## iter 80 value 621.558741
## iter 90 value 592.998099
## iter 100 value 533.299534
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## iter 470 value 345.752414
## iter 480 value 345.748892
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## iter 500 value 345.737004
## final value 345.737004
## stopped after 500 iterations
## # weights: 141
## initial value 1413241.499437
## iter 10 value 2279.795097
## iter 20 value 1175.898175
## iter 30 value 976.188393
## iter 40 value 811.499283
## iter 50 value 695.977927
## iter 60 value 640.718240
## iter 70 value 585.698489
## iter 80 value 533.511471
## iter 90 value 490.506825
## iter 100 value 458.070088
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## iter 120 value 386.702555
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## iter 140 value 321.730384
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## iter 160 value 290.376928
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## iter 220 value 242.318429
## iter 230 value 235.534123
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## iter 460 value 180.226224
## iter 470 value 179.673985
## iter 480 value 178.550208
## iter 490 value 177.661683
## iter 500 value 177.302370
## final value 177.302370
## stopped after 500 iterations
## # weights: 15
## initial value 1398267.615428
## iter 10 value 11619.407758
## iter 20 value 5352.990587
## iter 30 value 4647.506039
## iter 40 value 4461.688077
## iter 50 value 4432.359454
## iter 60 value 4408.846050
## iter 70 value 3999.030467
## iter 80 value 3908.154365
## iter 90 value 3897.943932
## iter 100 value 3892.703329
## iter 110 value 3892.294242
## iter 120 value 3886.270935
## iter 130 value 3870.236258
## iter 140 value 3864.745158
## iter 150 value 3818.714913
## iter 160 value 3758.256686
## iter 170 value 3734.727130
## iter 180 value 3711.406103
## iter 190 value 3215.751492
## iter 200 value 2259.044848
## iter 210 value 1709.045601
## iter 220 value 1645.635927
## iter 230 value 1592.875361
## iter 240 value 1564.872231
## iter 250 value 1551.023979
## iter 260 value 1547.415262
## iter 270 value 1539.517998
## iter 280 value 1537.306871
## iter 290 value 1536.106282
## iter 300 value 1533.038290
## iter 310 value 1531.696399
## iter 320 value 1531.654871
## iter 330 value 1529.604686
## iter 340 value 1528.612561
## iter 350 value 1528.599485
## iter 360 value 1528.008299
## iter 370 value 1527.704097
## final value 1527.704063
## converged
## # weights: 36
## initial value 1425884.984400
## iter 10 value 4274.641900
## iter 20 value 3328.818414
## iter 30 value 2467.323987
## iter 40 value 1882.492559
## iter 50 value 1753.893489
## iter 60 value 1724.666896
## iter 70 value 1664.142208
## iter 80 value 1557.235427
## iter 90 value 1454.255077
## iter 100 value 1379.264502
## iter 110 value 1320.661067
## iter 120 value 1279.833239
## iter 130 value 1255.141347
## iter 140 value 1248.975435
## iter 150 value 1247.564827
## iter 160 value 1240.850835
## iter 170 value 1236.421566
## iter 180 value 1234.224291
## iter 190 value 1232.937590
## iter 200 value 1231.782910
## iter 210 value 1231.164421
## iter 220 value 1231.143689
## iter 230 value 1230.912328
## iter 240 value 1230.587594
## iter 250 value 1230.338139
## iter 260 value 1230.121232
## iter 270 value 1229.762269
## iter 280 value 1229.483677
## iter 290 value 1229.281246
## iter 300 value 1229.144030
## iter 310 value 1229.014965
## iter 320 value 1228.977646
## iter 330 value 1228.805885
## iter 340 value 1228.563171
## iter 350 value 1228.545104
## final value 1228.540649
## converged
## # weights: 71
## initial value 1400456.740564
## iter 10 value 3769.503148
## iter 20 value 1609.621536
## iter 30 value 1132.550349
## iter 40 value 961.110155
## iter 50 value 917.997930
## iter 60 value 862.524135
## iter 70 value 809.792333
## iter 80 value 780.232878
## iter 90 value 754.344792
## iter 100 value 738.271386
## iter 110 value 717.508006
## iter 120 value 695.799373
## iter 130 value 674.496613
## iter 140 value 630.817995
## iter 150 value 591.225520
## iter 160 value 576.693149
## iter 170 value 567.232258
## iter 180 value 565.322388
## iter 190 value 564.212179
## iter 200 value 563.463263
## iter 210 value 562.087923
## iter 220 value 561.191284
## iter 230 value 560.446339
## iter 240 value 560.171669
## iter 250 value 559.893370
## iter 260 value 559.576179
## iter 270 value 559.265503
## iter 280 value 558.987343
## iter 290 value 558.599411
## iter 300 value 557.590485
## iter 310 value 557.261948
## iter 320 value 556.330804
## iter 330 value 555.574914
## iter 340 value 555.550285
## iter 350 value 555.535896
## iter 360 value 555.505884
## final value 555.482162
## converged
## # weights: 106
## initial value 1336939.781029
## iter 10 value 1754.972796
## iter 20 value 1094.500757
## iter 30 value 936.752908
## iter 40 value 834.680610
## iter 50 value 750.982570
## iter 60 value 700.301408
## iter 70 value 651.567954
## iter 80 value 626.308397
## iter 90 value 585.727432
## iter 100 value 548.558212
## iter 110 value 516.426520
## iter 120 value 491.223989
## iter 130 value 479.461800
## iter 140 value 466.830406
## iter 150 value 450.233048
## iter 160 value 439.074105
## iter 170 value 429.181798
## iter 180 value 410.458859
## iter 190 value 401.622316
## iter 200 value 395.574096
## iter 210 value 391.355808
## iter 220 value 387.697246
## iter 230 value 386.481693
## iter 240 value 384.761524
## iter 250 value 381.761226
## iter 260 value 374.295363
## iter 270 value 367.077224
## iter 280 value 361.753023
## iter 290 value 355.441133
## iter 300 value 345.515320
## iter 310 value 339.600873
## iter 320 value 335.162353
## iter 330 value 333.390948
## iter 340 value 331.884928
## iter 350 value 330.990934
## iter 360 value 330.775421
## iter 370 value 330.127361
## iter 380 value 329.441873
## iter 390 value 329.128377
## iter 400 value 328.835852
## iter 410 value 328.569258
## iter 420 value 328.534451
## iter 430 value 328.390357
## iter 440 value 328.262871
## iter 450 value 328.168906
## iter 460 value 328.053485
## iter 470 value 328.010757
## iter 480 value 327.980793
## iter 490 value 327.918547
## iter 500 value 327.753972
## final value 327.753972
## stopped after 500 iterations
## # weights: 141
## initial value 1374961.131929
## iter 10 value 1831.333931
## iter 20 value 1100.759513
## iter 30 value 850.947399
## iter 40 value 734.750064
## iter 50 value 623.429004
## iter 60 value 572.232199
## iter 70 value 527.814731
## iter 80 value 492.226171
## iter 90 value 467.169913
## iter 100 value 442.971125
## iter 110 value 408.700256
## iter 120 value 376.701035
## iter 130 value 345.284574
## iter 140 value 322.484092
## iter 150 value 304.947307
## iter 160 value 289.332505
## iter 170 value 274.862248
## iter 180 value 266.611418
## iter 190 value 254.682939
## iter 200 value 248.400356
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## iter 230 value 234.176118
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## iter 250 value 222.266938
## iter 260 value 216.766306
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## iter 280 value 209.641375
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## iter 300 value 208.443785
## iter 310 value 207.661347
## iter 320 value 206.788811
## iter 330 value 205.520869
## iter 340 value 204.476159
## iter 350 value 203.697017
## iter 360 value 201.833853
## iter 370 value 197.870000
## iter 380 value 195.701197
## iter 390 value 194.809276
## iter 400 value 194.326362
## iter 410 value 192.675713
## iter 420 value 189.786198
## iter 430 value 187.513462
## iter 440 value 186.848410
## iter 450 value 186.559953
## iter 460 value 186.393485
## iter 470 value 186.305587
## iter 480 value 185.760986
## iter 490 value 184.894006
## iter 500 value 184.529925
## final value 184.529925
## stopped after 500 iterations
## # weights: 15
## initial value 1424983.074507
## iter 10 value 3184.335134
## iter 20 value 1856.914509
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## iter 420 value 1326.426259
## iter 430 value 1325.969052
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## iter 450 value 1325.736175
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## iter 470 value 1325.388595
## iter 480 value 1325.279220
## iter 490 value 1324.516932
## iter 500 value 1324.462657
## final value 1324.462657
## stopped after 500 iterations
## # weights: 36
## initial value 1421277.795807
## iter 10 value 15328.252597
## iter 20 value 6906.319879
## iter 30 value 3875.178882
## iter 40 value 1812.569604
## iter 50 value 1321.756770
## iter 60 value 1136.043716
## iter 70 value 1032.106810
## iter 80 value 1019.772689
## iter 90 value 1015.064454
## iter 100 value 995.723278
## iter 110 value 981.964414
## iter 120 value 973.206297
## iter 130 value 967.615874
## iter 140 value 963.991324
## iter 150 value 962.004563
## iter 160 value 961.965358
## iter 170 value 961.634283
## iter 180 value 961.358432
## iter 190 value 961.129678
## iter 200 value 960.241016
## iter 210 value 958.877263
## iter 220 value 957.420689
## iter 230 value 957.398436
## iter 240 value 957.360362
## iter 250 value 957.269326
## iter 260 value 957.204316
## iter 270 value 956.987902
## iter 280 value 956.829697
## iter 290 value 954.517657
## iter 300 value 954.037585
## iter 310 value 953.946375
## iter 320 value 953.563411
## iter 330 value 953.352571
## iter 340 value 953.318728
## iter 350 value 952.635606
## iter 360 value 952.249214
## iter 370 value 951.955832
## iter 380 value 951.943652
## iter 390 value 951.917819
## iter 400 value 951.819838
## iter 410 value 951.793436
## iter 420 value 951.721970
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## iter 440 value 951.548690
## iter 450 value 951.546125
## iter 450 value 951.546118
## iter 450 value 951.546118
## final value 951.546118
## converged
## # weights: 71
## initial value 1366521.387012
## iter 10 value 1350.201908
## iter 20 value 1016.016646
## iter 30 value 917.464908
## iter 40 value 870.256786
## iter 50 value 828.985060
## iter 60 value 799.745985
## iter 70 value 776.622199
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## iter 100 value 698.342625
## iter 110 value 684.419145
## iter 120 value 676.521419
## iter 130 value 667.144859
## iter 140 value 661.673303
## iter 150 value 658.957310
## iter 160 value 655.500146
## iter 170 value 649.196530
## iter 180 value 643.154262
## iter 190 value 640.242539
## iter 200 value 635.569303
## iter 210 value 626.713164
## iter 220 value 612.017961
## iter 230 value 598.976373
## iter 240 value 588.704444
## iter 250 value 583.014381
## iter 260 value 577.745791
## iter 270 value 574.534050
## iter 280 value 573.606309
## iter 290 value 572.711124
## iter 300 value 572.486275
## iter 310 value 572.223319
## iter 320 value 572.122395
## iter 330 value 571.617871
## iter 340 value 569.738609
## iter 350 value 568.636824
## iter 360 value 568.270575
## iter 370 value 568.001508
## iter 380 value 567.830627
## iter 390 value 567.737005
## iter 400 value 567.647736
## iter 410 value 567.033922
## iter 420 value 559.613023
## iter 430 value 558.751537
## iter 440 value 558.702739
## iter 450 value 558.666222
## iter 460 value 558.609457
## iter 470 value 558.563013
## iter 480 value 558.447521
## iter 490 value 558.012973
## iter 500 value 557.754738
## final value 557.754738
## stopped after 500 iterations
## # weights: 106
## initial value 1412090.511358
## iter 10 value 1494.175724
## iter 20 value 1053.236614
## iter 30 value 926.095057
## iter 40 value 800.034680
## iter 50 value 740.946067
## iter 60 value 706.851837
## iter 70 value 673.778461
## iter 80 value 645.859827
## iter 90 value 599.567343
## iter 100 value 567.635545
## iter 110 value 542.638717
## iter 120 value 514.956111
## iter 130 value 495.128135
## iter 140 value 477.895883
## iter 150 value 460.157152
## iter 160 value 443.890560
## iter 170 value 428.705097
## iter 180 value 419.989287
## iter 190 value 414.454392
## iter 200 value 410.785414
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## iter 220 value 404.051716
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## iter 240 value 399.147165
## iter 250 value 395.577415
## iter 260 value 389.831574
## iter 270 value 385.342418
## iter 280 value 379.871588
## iter 290 value 373.750177
## iter 300 value 364.282784
## iter 310 value 356.193861
## iter 320 value 348.855263
## iter 330 value 342.435595
## iter 340 value 338.603993
## iter 350 value 336.808226
## iter 360 value 333.488791
## iter 370 value 329.659067
## iter 380 value 328.158702
## iter 390 value 326.845605
## iter 400 value 326.081922
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## iter 470 value 324.131057
## iter 480 value 323.876405
## iter 490 value 323.477908
## iter 500 value 323.191407
## final value 323.191407
## stopped after 500 iterations
## # weights: 141
## initial value 1435279.203886
## iter 10 value 1863.870093
## iter 20 value 1006.341184
## iter 30 value 859.730613
## iter 40 value 735.984331
## iter 50 value 664.509105
## iter 60 value 612.967306
## iter 70 value 555.417067
## iter 80 value 506.920627
## iter 90 value 477.093636
## iter 100 value 459.829205
## iter 110 value 438.643502
## iter 120 value 405.989912
## iter 130 value 374.277064
## iter 140 value 341.918017
## iter 150 value 320.991637
## iter 160 value 304.919845
## iter 170 value 284.026379
## iter 180 value 271.731189
## iter 190 value 266.276569
## iter 200 value 256.866124
## iter 210 value 248.634888
## iter 220 value 240.094533
## iter 230 value 235.503319
## iter 240 value 231.726380
## iter 250 value 226.511500
## iter 260 value 219.410012
## iter 270 value 210.476832
## iter 280 value 202.949763
## iter 290 value 200.197208
## iter 300 value 198.200849
## iter 310 value 195.750135
## iter 320 value 193.207088
## iter 330 value 190.659507
## iter 340 value 188.951442
## iter 350 value 187.889466
## iter 360 value 186.311763
## iter 370 value 183.815835
## iter 380 value 180.897434
## iter 390 value 176.815870
## iter 400 value 173.410807
## iter 410 value 171.370605
## iter 420 value 168.742920
## iter 430 value 163.764221
## iter 440 value 159.835772
## iter 450 value 156.073842
## iter 460 value 153.724553
## iter 470 value 152.378150
## iter 480 value 151.592847
## iter 490 value 150.949857
## iter 500 value 150.180509
## final value 150.180509
## stopped after 500 iterations
## # weights: 15
## initial value 1421937.842268
## iter 10 value 9841.528333
## iter 20 value 6032.932905
## iter 30 value 5275.628985
## iter 40 value 4034.263567
## iter 50 value 2876.540130
## iter 60 value 2009.115668
## iter 70 value 1573.939919
## iter 80 value 1501.454923
## iter 90 value 1421.594909
## iter 100 value 1393.863434
## iter 110 value 1391.194916
## iter 120 value 1387.545279
## final value 1387.510043
## converged
## # weights: 36
## initial value 1418151.550676
## iter 10 value 51620.583755
## iter 20 value 9348.115332
## iter 30 value 6638.657634
## iter 40 value 3865.995839
## iter 50 value 2152.095467
## iter 60 value 1750.828106
## iter 70 value 1543.496365
## iter 80 value 1431.066410
## iter 90 value 1331.637340
## iter 100 value 1287.197586
## iter 110 value 1253.961821
## iter 120 value 1210.168873
## iter 130 value 1193.841768
## iter 140 value 1187.301892
## iter 150 value 1174.107301
## iter 160 value 1167.790235
## iter 170 value 1166.767672
## iter 180 value 1165.580066
## iter 190 value 1162.215981
## iter 200 value 1160.108590
## iter 210 value 1157.934453
## iter 220 value 1148.951332
## iter 230 value 1144.488777
## iter 240 value 1141.751269
## iter 250 value 1139.510403
## iter 260 value 1135.217482
## iter 270 value 1130.786250
## iter 280 value 1125.646633
## iter 290 value 1119.261157
## iter 300 value 1117.765979
## iter 310 value 1117.594749
## iter 320 value 1117.567856
## iter 330 value 1117.540571
## iter 340 value 1117.531894
## final value 1117.531173
## converged
## # weights: 71
## initial value 1367285.629532
## iter 10 value 4801.349716
## iter 20 value 3483.713084
## iter 30 value 3187.372938
## iter 40 value 2924.200310
## iter 50 value 2734.437484
## iter 60 value 2413.925923
## iter 70 value 2029.980447
## iter 80 value 1731.364718
## iter 90 value 1589.571834
## iter 100 value 1453.117213
## iter 110 value 1301.421105
## iter 120 value 1218.340091
## iter 130 value 1153.770736
## iter 140 value 1099.645962
## iter 150 value 1085.445809
## iter 160 value 1068.441741
## iter 170 value 1050.156267
## iter 180 value 1035.094906
## iter 190 value 1026.180947
## iter 200 value 1010.802956
## iter 210 value 999.352440
## iter 220 value 996.212885
## iter 230 value 986.084437
## iter 240 value 963.456190
## iter 250 value 945.103155
## iter 260 value 925.946221
## iter 270 value 909.731650
## iter 280 value 890.317120
## iter 290 value 882.789164
## iter 300 value 880.530894
## iter 310 value 878.775762
## iter 320 value 877.310864
## iter 330 value 876.421988
## iter 340 value 875.626201
## iter 350 value 853.830309
## iter 360 value 837.970615
## iter 370 value 832.484980
## iter 380 value 825.408212
## iter 390 value 820.984671
## iter 400 value 818.375632
## iter 410 value 813.446804
## iter 420 value 810.367794
## iter 430 value 809.730352
## iter 440 value 809.720850
## iter 450 value 809.708653
## iter 460 value 809.698730
## iter 470 value 809.695645
## iter 480 value 809.694909
## final value 809.694894
## converged
## # weights: 106
## initial value 1383754.248993
## iter 10 value 1442.611223
## iter 20 value 1074.695873
## iter 30 value 981.517233
## iter 40 value 933.462850
## iter 50 value 870.021077
## iter 60 value 813.550223
## iter 70 value 752.773095
## iter 80 value 717.092668
## iter 90 value 694.078603
## iter 100 value 676.380635
## iter 110 value 664.318065
## iter 120 value 655.471418
## iter 130 value 643.805697
## iter 140 value 638.775514
## iter 150 value 635.443607
## iter 160 value 633.323309
## iter 170 value 632.041302
## iter 180 value 630.902589
## iter 190 value 630.213338
## iter 200 value 629.539486
## iter 210 value 628.806289
## iter 220 value 628.632929
## iter 230 value 628.260194
## iter 240 value 627.541860
## iter 250 value 626.862333
## iter 260 value 626.241576
## iter 270 value 625.595962
## iter 280 value 625.207543
## iter 290 value 625.013305
## iter 300 value 624.854849
## iter 310 value 624.678406
## iter 320 value 624.597537
## iter 330 value 624.583958
## iter 340 value 624.562869
## iter 350 value 623.928459
## iter 360 value 622.410993
## iter 370 value 621.958179
## iter 380 value 621.611626
## iter 390 value 621.402205
## iter 400 value 621.370432
## iter 410 value 621.353866
## iter 420 value 621.349589
## final value 621.349126
## converged
## # weights: 141
## initial value 1362691.192027
## iter 10 value 1396.214450
## iter 20 value 1053.304048
## iter 30 value 970.142883
## iter 40 value 900.699605
## iter 50 value 855.719454
## iter 60 value 822.132898
## iter 70 value 791.549221
## iter 80 value 748.934954
## iter 90 value 718.493823
## iter 100 value 692.455716
## iter 110 value 672.134362
## iter 120 value 660.140616
## iter 130 value 653.144535
## iter 140 value 644.336068
## iter 150 value 632.411751
## iter 160 value 608.486967
## iter 170 value 585.289480
## iter 180 value 564.997697
## iter 190 value 557.686968
## iter 200 value 550.520342
## iter 210 value 545.267709
## iter 220 value 540.856281
## iter 230 value 538.662005
## iter 240 value 537.619865
## iter 250 value 536.843202
## iter 260 value 535.521756
## iter 270 value 534.366532
## iter 280 value 533.712529
## iter 290 value 533.256397
## iter 300 value 532.443802
## iter 310 value 530.825332
## iter 320 value 530.163911
## iter 330 value 529.882426
## iter 340 value 529.649631
## iter 350 value 529.438600
## iter 360 value 528.904547
## iter 370 value 527.564948
## iter 380 value 525.683209
## iter 390 value 524.802460
## iter 400 value 524.140795
## iter 410 value 521.752954
## iter 420 value 513.387863
## iter 430 value 510.366055
## iter 440 value 508.452656
## iter 450 value 508.061287
## iter 460 value 507.943478
## iter 470 value 507.838333
## iter 480 value 507.814377
## iter 490 value 507.813459
## final value 507.813435
## converged
## # weights: 15
## initial value 1426595.928637
## iter 10 value 17017.878547
## iter 20 value 9090.032534
## iter 30 value 7990.107122
## iter 40 value 3925.163419
## iter 50 value 2626.191477
## iter 60 value 1916.954235
## iter 70 value 1707.395862
## iter 80 value 1663.693129
## iter 90 value 1625.374557
## iter 100 value 1612.579832
## iter 110 value 1604.971906
## iter 120 value 1583.369547
## iter 130 value 1572.311308
## iter 140 value 1557.353843
## iter 150 value 1496.937542
## iter 160 value 1458.404851
## iter 170 value 1413.605562
## iter 180 value 1411.493109
## iter 190 value 1410.764512
## iter 200 value 1407.259326
## final value 1406.632528
## converged
## # weights: 36
## initial value 1418371.926877
## iter 10 value 18916.628757
## iter 20 value 14428.087708
## iter 30 value 8836.216035
## iter 40 value 6193.600532
## iter 50 value 4847.058241
## iter 60 value 2727.566199
## iter 70 value 1579.207455
## iter 80 value 1273.087030
## iter 90 value 1197.786573
## iter 100 value 1117.201581
## iter 110 value 1047.415165
## iter 120 value 1005.820174
## iter 130 value 987.439754
## iter 140 value 977.710448
## iter 150 value 971.741813
## iter 160 value 971.254451
## iter 170 value 968.825270
## iter 180 value 957.729166
## iter 190 value 954.491699
## iter 200 value 948.678479
## iter 210 value 945.305976
## iter 220 value 942.935557
## iter 230 value 942.672985
## iter 240 value 942.065239
## iter 250 value 940.850766
## iter 260 value 940.337084
## iter 270 value 939.787963
## iter 280 value 939.373664
## iter 290 value 938.209345
## iter 300 value 937.556138
## iter 310 value 937.070763
## iter 320 value 935.447503
## iter 330 value 929.205554
## iter 340 value 926.801094
## iter 350 value 925.060744
## iter 360 value 918.935071
## iter 370 value 915.602477
## iter 380 value 914.461503
## iter 390 value 910.673473
## iter 400 value 907.777216
## iter 410 value 905.679867
## iter 420 value 901.733043
## iter 430 value 900.424816
## iter 440 value 900.082796
## iter 450 value 900.073318
## iter 460 value 900.063941
## iter 470 value 900.018861
## iter 480 value 899.935805
## iter 490 value 899.919352
## final value 899.917438
## converged
## # weights: 71
## initial value 1365783.165322
## iter 10 value 1738.285767
## iter 20 value 1104.327619
## iter 30 value 1015.964016
## iter 40 value 951.741409
## iter 50 value 905.605703
## iter 60 value 863.722931
## iter 70 value 832.741110
## iter 80 value 797.180052
## iter 90 value 770.857187
## iter 100 value 754.168714
## iter 110 value 745.598271
## iter 120 value 729.551934
## iter 130 value 712.645514
## iter 140 value 702.237575
## iter 150 value 698.939193
## iter 160 value 696.619563
## iter 170 value 694.132101
## iter 180 value 690.023416
## iter 190 value 680.254605
## iter 200 value 659.659904
## iter 210 value 651.656002
## iter 220 value 648.377889
## iter 230 value 645.582393
## iter 240 value 642.540150
## iter 250 value 637.746077
## iter 260 value 634.558705
## iter 270 value 631.761105
## iter 280 value 629.477412
## iter 290 value 624.818697
## iter 300 value 623.458960
## iter 310 value 622.297201
## iter 320 value 620.900142
## iter 330 value 618.351214
## iter 340 value 614.775985
## iter 350 value 613.663686
## iter 360 value 606.214610
## iter 370 value 603.134759
## iter 380 value 598.219786
## iter 390 value 596.867761
## iter 400 value 596.753512
## iter 410 value 596.692617
## iter 420 value 596.681170
## iter 430 value 596.677448
## final value 596.677417
## converged
## # weights: 106
## initial value 1403925.397441
## iter 10 value 1158.820793
## iter 20 value 979.543542
## iter 30 value 869.469869
## iter 40 value 755.283825
## iter 50 value 689.990563
## iter 60 value 629.020080
## iter 70 value 551.568211
## iter 80 value 500.112668
## iter 90 value 464.391972
## iter 100 value 433.144624
## iter 110 value 407.061326
## iter 120 value 393.616337
## iter 130 value 386.575665
## iter 140 value 383.499837
## iter 150 value 379.150474
## iter 160 value 373.352734
## iter 170 value 369.839224
## iter 180 value 362.430979
## iter 190 value 358.079622
## iter 200 value 354.408505
## iter 210 value 349.313274
## iter 220 value 348.651988
## iter 230 value 348.137856
## iter 240 value 347.508687
## iter 250 value 346.585487
## iter 260 value 345.159527
## iter 270 value 343.842136
## iter 280 value 342.261962
## iter 290 value 337.157496
## iter 300 value 334.125030
## iter 310 value 330.622591
## iter 320 value 323.880588
## iter 330 value 320.028270
## iter 340 value 314.262211
## iter 350 value 311.993419
## iter 360 value 309.377885
## iter 370 value 305.523619
## iter 380 value 302.722307
## iter 390 value 302.345489
## iter 400 value 302.126253
## iter 410 value 302.006545
## iter 420 value 301.899817
## iter 430 value 301.807418
## iter 440 value 301.798523
## iter 450 value 301.782906
## iter 460 value 301.710917
## iter 470 value 301.539631
## iter 480 value 301.482744
## iter 490 value 301.467992
## iter 500 value 301.436826
## final value 301.436826
## stopped after 500 iterations
## # weights: 141
## initial value 1344021.555710
## iter 10 value 1410.599884
## iter 20 value 926.415518
## iter 30 value 813.455961
## iter 40 value 749.896152
## iter 50 value 659.731260
## iter 60 value 588.791864
## iter 70 value 543.294616
## iter 80 value 502.239566
## iter 90 value 460.619564
## iter 100 value 436.474295
## iter 110 value 414.571104
## iter 120 value 402.924335
## iter 130 value 388.478877
## iter 140 value 375.464065
## iter 150 value 358.204972
## iter 160 value 334.757560
## iter 170 value 309.829527
## iter 180 value 296.616766
## iter 190 value 287.413895
## iter 200 value 277.235181
## iter 210 value 263.464152
## iter 220 value 251.810705
## iter 230 value 244.993638
## iter 240 value 236.692107
## iter 250 value 231.480771
## iter 260 value 229.002418
## iter 270 value 226.954971
## iter 280 value 223.887127
## iter 290 value 222.682037
## iter 300 value 222.141484
## iter 310 value 221.513557
## iter 320 value 220.535741
## iter 330 value 219.745105
## iter 340 value 218.763501
## iter 350 value 217.383479
## iter 360 value 216.164420
## iter 370 value 214.455334
## iter 380 value 212.248506
## iter 390 value 210.390765
## iter 400 value 209.102367
## iter 410 value 208.324314
## iter 420 value 207.475027
## iter 430 value 206.985703
## iter 440 value 206.556325
## iter 450 value 206.044048
## iter 460 value 204.989411
## iter 470 value 203.886669
## iter 480 value 202.093646
## iter 490 value 198.507242
## iter 500 value 196.978472
## final value 196.978472
## stopped after 500 iterations
## # weights: 15
## initial value 1403916.590347
## iter 10 value 19631.327003
## iter 20 value 16658.481632
## iter 30 value 16357.930964
## iter 40 value 13489.768771
## iter 50 value 9235.516530
## iter 60 value 8847.106372
## iter 70 value 8675.084162
## iter 80 value 8675.023715
## iter 90 value 8674.837734
## iter 100 value 8666.193536
## iter 110 value 8346.851523
## iter 120 value 4204.635979
## iter 130 value 2839.711124
## iter 140 value 2155.922053
## iter 150 value 1792.889848
## iter 160 value 1513.329598
## iter 170 value 1414.277991
## iter 180 value 1403.415451
## iter 190 value 1392.581874
## iter 200 value 1381.641742
## iter 210 value 1379.197087
## iter 220 value 1379.000313
## iter 230 value 1376.017257
## iter 240 value 1375.433271
## final value 1375.430641
## converged
## # weights: 36
## initial value 1375655.799795
## iter 10 value 3675.595029
## iter 20 value 2812.122350
## iter 30 value 2095.996179
## iter 40 value 1496.622637
## iter 50 value 1230.828022
## iter 60 value 1055.970831
## iter 70 value 992.355653
## iter 80 value 964.616021
## iter 90 value 956.936073
## iter 100 value 956.420746
## iter 110 value 954.259685
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## iter 390 value 811.934537
## iter 400 value 811.205739
## iter 410 value 811.071141
## iter 420 value 810.037752
## final value 809.999876
## converged
## # weights: 71
## initial value 1364099.852105
## iter 10 value 1549.214879
## iter 20 value 1163.111642
## iter 30 value 984.281810
## iter 40 value 907.080865
## iter 50 value 818.234327
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## iter 250 value 619.376607
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## iter 280 value 613.685388
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## iter 470 value 604.848541
## iter 480 value 604.843834
## iter 490 value 604.833962
## iter 500 value 604.824480
## final value 604.824480
## stopped after 500 iterations
## # weights: 106
## initial value 1395136.924542
## iter 10 value 1416.602984
## iter 20 value 999.944528
## iter 30 value 874.796497
## iter 40 value 786.464772
## iter 50 value 749.288057
## iter 60 value 719.091860
## iter 70 value 663.744209
## iter 80 value 594.920985
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## iter 300 value 322.783923
## iter 310 value 318.275317
## iter 320 value 314.209133
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## iter 470 value 288.696329
## iter 480 value 288.689396
## iter 490 value 288.684777
## iter 500 value 288.680206
## final value 288.680206
## stopped after 500 iterations
## # weights: 141
## initial value 1406772.662054
## iter 10 value 1714.478941
## iter 20 value 1036.595551
## iter 30 value 828.527235
## iter 40 value 737.638283
## iter 50 value 657.972671
## iter 60 value 573.349167
## iter 70 value 513.670059
## iter 80 value 458.568104
## iter 90 value 429.918241
## iter 100 value 399.396350
## iter 110 value 377.409826
## iter 120 value 357.143173
## iter 130 value 338.988724
## iter 140 value 326.856070
## iter 150 value 312.281079
## iter 160 value 297.027342
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## iter 180 value 271.572784
## iter 190 value 266.151141
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## iter 220 value 255.927316
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## iter 300 value 241.193416
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## iter 360 value 233.970471
## iter 370 value 228.766464
## iter 380 value 223.061501
## iter 390 value 215.269419
## iter 400 value 210.188187
## iter 410 value 206.622860
## iter 420 value 202.827905
## iter 430 value 199.256243
## iter 440 value 197.269270
## iter 450 value 195.136679
## iter 460 value 193.950529
## iter 470 value 192.737697
## iter 480 value 191.669596
## iter 490 value 190.516192
## iter 500 value 189.397834
## final value 189.397834
## stopped after 500 iterations
## # weights: 15
## initial value 1376674.377536
## iter 10 value 5621.843818
## iter 20 value 5104.331639
## iter 30 value 4853.081946
## iter 40 value 4205.320797
## iter 50 value 3733.986549
## iter 60 value 3707.050660
## iter 70 value 3699.653566
## iter 80 value 3696.468391
## iter 90 value 3501.961455
## iter 100 value 2624.958484
## iter 110 value 1821.000497
## iter 120 value 1668.395432
## iter 130 value 1523.374573
## iter 140 value 1464.809582
## iter 150 value 1448.631549
## iter 160 value 1442.852875
## iter 170 value 1433.152033
## iter 180 value 1429.959829
## iter 190 value 1429.624015
## iter 200 value 1425.968961
## iter 210 value 1424.744834
## iter 220 value 1424.740764
## iter 220 value 1424.740753
## iter 220 value 1424.740746
## final value 1424.740746
## converged
## # weights: 36
## initial value 1382691.125324
## iter 10 value 9947.002188
## iter 20 value 5546.759036
## iter 30 value 3929.123987
## iter 40 value 2792.684034
## iter 50 value 2099.202778
## iter 60 value 1792.648877
## iter 70 value 1711.009906
## iter 80 value 1598.733324
## iter 90 value 1535.649846
## iter 100 value 1503.335612
## iter 110 value 1491.616080
## iter 120 value 1490.686889
## iter 130 value 1488.842807
## iter 140 value 1486.488297
## iter 150 value 1484.885798
## iter 160 value 1484.331113
## iter 170 value 1483.932454
## iter 180 value 1483.654989
## iter 190 value 1483.578155
## final value 1483.577780
## converged
## # weights: 71
## initial value 1430181.745343
## iter 10 value 1359.795027
## iter 20 value 1025.856086
## iter 30 value 906.949488
## iter 40 value 858.445484
## iter 50 value 821.349893
## iter 60 value 804.166791
## iter 70 value 786.964711
## iter 80 value 774.083679
## iter 90 value 759.624385
## iter 100 value 748.166369
## iter 110 value 731.474802
## iter 120 value 708.467501
## iter 130 value 692.361138
## iter 140 value 671.358734
## iter 150 value 647.852048
## iter 160 value 633.198745
## iter 170 value 622.539341
## iter 180 value 610.526348
## iter 190 value 587.406880
## iter 200 value 568.933425
## iter 210 value 557.365421
## iter 220 value 552.371952
## iter 230 value 545.880122
## iter 240 value 541.438773
## iter 250 value 537.443648
## iter 260 value 533.742008
## iter 270 value 529.072583
## iter 280 value 524.360143
## iter 290 value 520.624747
## iter 300 value 520.093739
## iter 310 value 519.648874
## iter 320 value 517.996409
## iter 330 value 515.616384
## iter 340 value 514.733478
## iter 350 value 512.284304
## iter 360 value 508.831928
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## iter 380 value 495.850960
## iter 390 value 495.584841
## iter 400 value 495.272253
## iter 410 value 494.583450
## iter 420 value 493.893843
## iter 430 value 487.262347
## iter 440 value 479.386878
## iter 450 value 476.195143
## iter 460 value 475.823571
## iter 470 value 474.834714
## iter 480 value 472.634940
## iter 490 value 472.133852
## iter 500 value 471.703563
## final value 471.703563
## stopped after 500 iterations
## # weights: 106
## initial value 1388346.535000
## iter 10 value 1878.003060
## iter 20 value 1036.019015
## iter 30 value 828.655095
## iter 40 value 722.873776
## iter 50 value 639.073632
## iter 60 value 576.470101
## iter 70 value 536.501313
## iter 80 value 502.909736
## iter 90 value 471.026188
## iter 100 value 452.279119
## iter 110 value 430.847087
## iter 120 value 409.072072
## iter 130 value 391.067232
## iter 140 value 367.668051
## iter 150 value 354.775451
## iter 160 value 350.715116
## iter 170 value 346.854163
## iter 180 value 342.895854
## iter 190 value 336.566826
## iter 200 value 331.410359
## iter 210 value 327.821525
## iter 220 value 326.444663
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## iter 240 value 323.952988
## iter 250 value 321.084134
## iter 260 value 317.119038
## iter 270 value 312.248271
## iter 280 value 309.532801
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## iter 300 value 298.926932
## iter 310 value 295.955197
## iter 320 value 292.231072
## iter 330 value 289.440307
## iter 340 value 287.694625
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## iter 470 value 283.880113
## iter 480 value 283.875202
## iter 490 value 283.872609
## iter 500 value 283.863770
## final value 283.863770
## stopped after 500 iterations
## # weights: 141
## initial value 1348136.615119
## iter 10 value 1348.404639
## iter 20 value 1036.739033
## iter 30 value 912.863847
## iter 40 value 811.962117
## iter 50 value 678.852316
## iter 60 value 596.000188
## iter 70 value 540.074767
## iter 80 value 500.050711
## iter 90 value 437.829197
## iter 100 value 408.624370
## iter 110 value 391.100221
## iter 120 value 375.635385
## iter 130 value 354.295110
## iter 140 value 333.486763
## iter 150 value 310.830014
## iter 160 value 288.283947
## iter 170 value 274.981833
## iter 180 value 263.238701
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## iter 250 value 207.781910
## iter 260 value 200.514124
## iter 270 value 198.047747
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## iter 290 value 194.581800
## iter 300 value 193.827179
## iter 310 value 192.737067
## iter 320 value 191.844497
## iter 330 value 190.354508
## iter 340 value 188.709876
## iter 350 value 186.701189
## iter 360 value 184.898027
## iter 370 value 183.576098
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## iter 390 value 177.261433
## iter 400 value 173.329196
## iter 410 value 170.711185
## iter 420 value 167.688026
## iter 430 value 165.320267
## iter 440 value 162.658250
## iter 450 value 160.712742
## iter 460 value 159.937916
## iter 470 value 159.337227
## iter 480 value 158.947436
## iter 490 value 157.987249
## iter 500 value 157.122143
## final value 157.122143
## stopped after 500 iterations
## # weights: 15
## initial value 1427661.626729
## iter 10 value 13793.275941
## iter 20 value 9502.033391
## iter 30 value 6797.213984
## iter 40 value 4628.285184
## iter 50 value 2349.210858
## iter 60 value 1766.809270
## iter 70 value 1765.220716
## iter 80 value 1757.092608
## iter 90 value 1754.417945
## iter 100 value 1753.521984
## iter 110 value 1752.527095
## iter 120 value 1751.923661
## iter 130 value 1751.858340
## iter 130 value 1751.858335
## final value 1751.858194
## converged
## # weights: 36
## initial value 1377628.891625
## iter 10 value 3470.726041
## iter 20 value 2442.297627
## iter 30 value 2244.466817
## iter 40 value 2037.517612
## iter 50 value 2012.806539
## iter 60 value 1995.958433
## iter 70 value 1747.585129
## iter 80 value 1536.446254
## iter 90 value 1401.129600
## iter 100 value 1369.280905
## iter 110 value 1339.565971
## iter 120 value 1263.680829
## iter 130 value 1226.559174
## iter 140 value 1197.998205
## iter 150 value 1178.277910
## iter 160 value 1099.061407
## iter 170 value 1056.115049
## iter 180 value 1030.073685
## iter 190 value 1023.941405
## iter 200 value 1021.349971
## iter 210 value 1012.492388
## iter 220 value 1008.327586
## iter 230 value 1000.741985
## iter 240 value 992.335732
## iter 250 value 977.904853
## iter 260 value 971.722418
## iter 270 value 971.104288
## iter 280 value 968.396919
## iter 290 value 965.322713
## iter 300 value 940.668319
## iter 310 value 935.363706
## iter 320 value 933.921653
## iter 330 value 933.426040
## iter 340 value 932.297846
## iter 350 value 931.460782
## iter 360 value 931.426861
## iter 370 value 931.241459
## iter 380 value 930.841915
## iter 390 value 929.712963
## iter 400 value 927.864537
## iter 410 value 926.110200
## iter 420 value 922.295063
## iter 430 value 921.912459
## iter 440 value 921.830175
## iter 450 value 921.455080
## iter 460 value 921.227791
## iter 470 value 921.132481
## iter 480 value 920.601806
## iter 490 value 920.344186
## iter 500 value 920.122982
## final value 920.122982
## stopped after 500 iterations
## # weights: 71
## initial value 1441814.345653
## iter 10 value 8964.553894
## iter 20 value 2697.397961
## iter 30 value 1642.198570
## iter 40 value 1315.795553
## iter 50 value 1142.534114
## iter 60 value 1055.405087
## iter 70 value 1000.562003
## iter 80 value 964.549049
## iter 90 value 930.033377
## iter 100 value 910.566422
## iter 110 value 880.577192
## iter 120 value 864.358830
## iter 130 value 857.540191
## iter 140 value 845.504482
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## iter 160 value 840.213392
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## iter 180 value 837.440665
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## iter 200 value 828.854726
## iter 210 value 822.670607
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## iter 240 value 811.942242
## iter 250 value 805.927616
## iter 260 value 800.333793
## iter 270 value 793.226324
## iter 280 value 789.426458
## iter 290 value 787.449142
## iter 300 value 786.925715
## iter 310 value 785.736328
## iter 320 value 780.815440
## iter 330 value 768.760920
## iter 340 value 751.616856
## iter 350 value 747.376681
## iter 360 value 745.859932
## iter 370 value 744.337745
## iter 380 value 743.109826
## iter 390 value 742.009836
## iter 400 value 741.023646
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## iter 420 value 739.409954
## iter 430 value 739.286394
## iter 440 value 739.284262
## iter 450 value 739.283163
## iter 460 value 739.281257
## iter 470 value 739.278146
## iter 480 value 739.267116
## iter 490 value 739.244156
## iter 500 value 739.171731
## final value 739.171731
## stopped after 500 iterations
## # weights: 106
## initial value 1434450.161944
## iter 10 value 1277.871972
## iter 20 value 1017.304972
## iter 30 value 892.734701
## iter 40 value 800.416988
## iter 50 value 729.309155
## iter 60 value 679.817039
## iter 70 value 619.490133
## iter 80 value 578.488873
## iter 90 value 551.223548
## iter 100 value 531.676203
## iter 110 value 512.843881
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## iter 140 value 467.021798
## iter 150 value 443.632608
## iter 160 value 423.438251
## iter 170 value 413.895787
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## iter 190 value 378.415858
## iter 200 value 367.661738
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## iter 220 value 349.835078
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## iter 250 value 339.776803
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## iter 300 value 321.529210
## iter 310 value 318.370870
## iter 320 value 314.113507
## iter 330 value 306.837927
## iter 340 value 299.641913
## iter 350 value 295.992983
## iter 360 value 293.938729
## iter 370 value 292.628408
## iter 380 value 291.184495
## iter 390 value 290.785342
## iter 400 value 290.666830
## iter 410 value 290.474187
## iter 420 value 290.382428
## iter 430 value 290.354936
## iter 440 value 290.352191
## iter 450 value 290.345442
## iter 460 value 290.339592
## iter 470 value 290.332895
## iter 480 value 290.309380
## iter 490 value 290.227195
## iter 500 value 290.114493
## final value 290.114493
## stopped after 500 iterations
## # weights: 141
## initial value 1412825.912914
## iter 10 value 1622.287227
## iter 20 value 1027.175289
## iter 30 value 933.843782
## iter 40 value 816.994609
## iter 50 value 728.817589
## iter 60 value 668.545782
## iter 70 value 612.028895
## iter 80 value 561.048547
## iter 90 value 519.495084
## iter 100 value 483.047482
## iter 110 value 453.567890
## iter 120 value 422.274667
## iter 130 value 402.452536
## iter 140 value 363.881929
## iter 150 value 342.392514
## iter 160 value 323.615831
## iter 170 value 313.252920
## iter 180 value 300.711601
## iter 190 value 289.230853
## iter 200 value 279.521352
## iter 210 value 267.254278
## iter 220 value 262.369089
## iter 230 value 253.680932
## iter 240 value 246.146566
## iter 250 value 236.879230
## iter 260 value 227.189029
## iter 270 value 219.682763
## iter 280 value 212.605946
## iter 290 value 209.810365
## iter 300 value 207.865112
## iter 310 value 204.917534
## iter 320 value 202.974789
## iter 330 value 200.721545
## iter 340 value 198.647181
## iter 350 value 197.305454
## iter 360 value 195.876452
## iter 370 value 192.568266
## iter 380 value 190.440122
## iter 390 value 187.374904
## iter 400 value 183.581968
## iter 410 value 180.260881
## iter 420 value 177.493354
## iter 430 value 174.679135
## iter 440 value 173.046095
## iter 450 value 172.056058
## iter 460 value 171.588915
## iter 470 value 171.196504
## iter 480 value 170.993620
## iter 490 value 170.795823
## iter 500 value 170.050674
## final value 170.050674
## stopped after 500 iterations
## # weights: 15
## initial value 1408986.934906
## iter 10 value 20985.853377
## iter 20 value 18459.320081
## iter 30 value 14466.829187
## iter 40 value 10360.851669
## iter 50 value 6077.681780
## iter 60 value 3193.120858
## iter 70 value 2710.417361
## iter 80 value 2015.950262
## iter 90 value 1944.507555
## iter 100 value 1944.268906
## final value 1943.770161
## converged
## # weights: 36
## initial value 1424952.463072
## iter 10 value 5181.022819
## iter 20 value 2990.784576
## iter 30 value 2146.527377
## iter 40 value 1792.535196
## iter 50 value 1679.140131
## iter 60 value 1482.584543
## iter 70 value 1407.448429
## iter 80 value 1363.953319
## iter 90 value 1309.425001
## iter 100 value 1281.526493
## iter 110 value 1277.619300
## iter 120 value 1276.951650
## iter 130 value 1275.365347
## iter 140 value 1275.113500
## final value 1275.112288
## converged
## # weights: 71
## initial value 1429060.172372
## iter 10 value 1418.147438
## iter 20 value 1193.100841
## iter 30 value 1138.425928
## iter 40 value 1109.584556
## iter 50 value 1080.077597
## iter 60 value 1035.973241
## iter 70 value 974.134717
## iter 80 value 938.002297
## iter 90 value 918.991655
## iter 100 value 910.385064
## iter 110 value 897.278739
## iter 120 value 880.451020
## iter 130 value 871.325790
## iter 140 value 867.609328
## iter 150 value 866.531714
## iter 160 value 864.909898
## iter 170 value 862.806475
## iter 180 value 860.059381
## iter 190 value 852.411528
## iter 200 value 843.481627
## iter 210 value 839.922647
## iter 220 value 837.711992
## iter 230 value 831.224292
## iter 240 value 827.610699
## iter 250 value 821.150431
## iter 260 value 818.464343
## iter 270 value 815.260980
## iter 280 value 811.319243
## iter 290 value 805.509097
## iter 300 value 804.189739
## iter 310 value 802.524599
## iter 320 value 798.877375
## iter 330 value 794.785623
## iter 340 value 790.469847
## iter 350 value 787.312113
## iter 360 value 786.128768
## iter 370 value 785.684914
## iter 380 value 785.606827
## final value 785.602927
## converged
## # weights: 106
## initial value 1457393.662670
## iter 10 value 1557.199836
## iter 20 value 1134.818062
## iter 30 value 999.912513
## iter 40 value 951.481670
## iter 50 value 897.201569
## iter 60 value 869.302980
## iter 70 value 826.885783
## iter 80 value 802.474842
## iter 90 value 777.673778
## iter 100 value 760.187391
## iter 110 value 742.766858
## iter 120 value 727.423780
## iter 130 value 718.035738
## iter 140 value 702.102235
## iter 150 value 684.594146
## iter 160 value 677.945601
## iter 170 value 669.492198
## iter 180 value 664.894441
## iter 190 value 662.344934
## iter 200 value 660.965928
## iter 210 value 658.859020
## iter 220 value 657.831002
## iter 230 value 656.324306
## iter 240 value 653.945843
## iter 250 value 650.616812
## iter 260 value 647.373679
## iter 270 value 644.620580
## iter 280 value 642.318794
## iter 290 value 640.374353
## iter 300 value 637.514473
## iter 310 value 624.212510
## iter 320 value 617.611809
## iter 330 value 615.647337
## iter 340 value 614.290170
## iter 350 value 608.560098
## iter 360 value 597.891043
## iter 370 value 595.209494
## iter 380 value 593.827338
## iter 390 value 593.094177
## iter 400 value 592.987570
## iter 410 value 592.965640
## iter 420 value 592.960258
## final value 592.959782
## converged
## # weights: 141
## initial value 1400810.069917
## iter 10 value 1799.311970
## iter 20 value 1303.511605
## iter 30 value 1027.560078
## iter 40 value 949.153520
## iter 50 value 874.085266
## iter 60 value 844.898199
## iter 70 value 809.437515
## iter 80 value 765.205522
## iter 90 value 746.127091
## iter 100 value 730.707761
## iter 110 value 718.157413
## iter 120 value 704.947451
## iter 130 value 694.985050
## iter 140 value 689.156792
## iter 150 value 683.970761
## iter 160 value 679.695849
## iter 170 value 675.859216
## iter 180 value 668.104260
## iter 190 value 651.416306
## iter 200 value 637.134357
## iter 210 value 625.783503
## iter 220 value 616.704054
## iter 230 value 608.045919
## iter 240 value 600.718818
## iter 250 value 595.242235
## iter 260 value 592.704917
## iter 270 value 590.925946
## iter 280 value 587.539662
## iter 290 value 583.880277
## iter 300 value 581.413736
## iter 310 value 575.973352
## iter 320 value 573.297888
## iter 330 value 571.296281
## iter 340 value 570.311583
## iter 350 value 568.923493
## iter 360 value 568.118303
## iter 370 value 566.588831
## iter 380 value 561.634314
## iter 390 value 560.163042
## iter 400 value 558.877240
## iter 410 value 557.867369
## iter 420 value 556.428619
## iter 430 value 555.584055
## iter 440 value 555.071903
## iter 450 value 554.859104
## iter 460 value 554.696828
## iter 470 value 554.690647
## final value 554.690363
## converged
## # weights: 15
## initial value 1447346.466397
## iter 10 value 9299.894054
## iter 20 value 3516.575673
## iter 30 value 2092.860369
## iter 40 value 1858.505191
## iter 50 value 1770.138144
## iter 60 value 1740.290535
## iter 70 value 1710.183812
## iter 80 value 1527.970253
## iter 90 value 1493.693263
## iter 100 value 1292.222793
## iter 110 value 1196.664669
## iter 120 value 1186.691979
## iter 130 value 1180.942644
## iter 140 value 1179.700850
## iter 150 value 1178.903426
## iter 160 value 1178.038924
## iter 170 value 1176.034017
## iter 180 value 1175.117315
## iter 190 value 1174.476149
## iter 200 value 1174.196668
## iter 210 value 1174.193115
## final value 1174.192520
## converged
## # weights: 36
## initial value 1356986.625888
## iter 10 value 7074.598401
## iter 20 value 2459.361402
## iter 30 value 1731.668905
## iter 40 value 1193.694142
## iter 50 value 1037.538754
## iter 60 value 1004.325158
## iter 70 value 994.277430
## iter 80 value 990.472406
## iter 90 value 988.878079
## iter 100 value 982.039727
## iter 110 value 970.323716
## iter 120 value 955.247332
## iter 130 value 944.909823
## iter 140 value 931.231258
## iter 150 value 912.187056
## iter 160 value 901.647812
## iter 170 value 895.922190
## iter 180 value 889.055226
## iter 190 value 878.372436
## iter 200 value 873.199637
## iter 210 value 870.405637
## iter 220 value 869.984116
## iter 230 value 869.918142
## iter 240 value 869.824494
## iter 250 value 869.746318
## iter 260 value 869.403499
## iter 270 value 869.397973
## final value 869.397452
## converged
## # weights: 71
## initial value 1417800.901559
## iter 10 value 1740.270332
## iter 20 value 1158.517677
## iter 30 value 1023.205752
## iter 40 value 926.083079
## iter 50 value 868.726272
## iter 60 value 825.349074
## iter 70 value 810.341172
## iter 80 value 751.334259
## iter 90 value 730.042979
## iter 100 value 697.275690
## iter 110 value 675.242850
## iter 120 value 656.822588
## iter 130 value 642.764232
## iter 140 value 622.291473
## iter 150 value 602.874378
## iter 160 value 592.151162
## iter 170 value 578.676084
## iter 180 value 561.857170
## iter 190 value 544.419308
## iter 200 value 534.891970
## iter 210 value 526.091616
## iter 220 value 517.149032
## iter 230 value 506.397388
## iter 240 value 502.883014
## iter 250 value 500.957554
## iter 260 value 498.713438
## iter 270 value 496.422961
## iter 280 value 494.281133
## iter 290 value 493.367401
## iter 300 value 493.171700
## iter 310 value 492.897882
## iter 320 value 492.590265
## iter 330 value 492.083835
## iter 340 value 490.771951
## iter 350 value 489.543882
## iter 360 value 489.238281
## iter 370 value 488.988331
## iter 380 value 488.937489
## iter 390 value 488.899004
## iter 400 value 488.888576
## iter 410 value 488.885926
## iter 420 value 488.885692
## final value 488.885685
## converged
## # weights: 106
## initial value 1374480.931723
## iter 10 value 1207.326961
## iter 20 value 1007.469962
## iter 30 value 895.488828
## iter 40 value 815.740049
## iter 50 value 752.202782
## iter 60 value 713.940912
## iter 70 value 662.738635
## iter 80 value 634.618804
## iter 90 value 614.331922
## iter 100 value 595.287286
## iter 110 value 578.044096
## iter 120 value 558.157726
## iter 130 value 541.312234
## iter 140 value 521.811302
## iter 150 value 499.493602
## iter 160 value 479.683491
## iter 170 value 464.004036
## iter 180 value 454.180065
## iter 190 value 446.839690
## iter 200 value 438.171123
## iter 210 value 423.998131
## iter 220 value 418.390025
## iter 230 value 415.771044
## iter 240 value 411.617170
## iter 250 value 403.315964
## iter 260 value 395.170007
## iter 270 value 388.060051
## iter 280 value 383.297416
## iter 290 value 378.403069
## iter 300 value 374.275601
## iter 310 value 370.498740
## iter 320 value 367.213614
## iter 330 value 366.268632
## iter 340 value 365.356791
## iter 350 value 365.140489
## iter 360 value 365.096208
## iter 370 value 365.086513
## iter 380 value 365.085364
## iter 390 value 365.085101
## final value 365.085093
## converged
## # weights: 141
## initial value 1457144.428539
## iter 10 value 1357.938142
## iter 20 value 1056.045132
## iter 30 value 925.175302
## iter 40 value 812.813513
## iter 50 value 750.331680
## iter 60 value 707.926008
## iter 70 value 653.277319
## iter 80 value 582.687107
## iter 90 value 536.159823
## iter 100 value 487.922223
## iter 110 value 458.811011
## iter 120 value 425.335333
## iter 130 value 388.791907
## iter 140 value 338.847453
## iter 150 value 320.730838
## iter 160 value 312.505654
## iter 170 value 298.349396
## iter 180 value 291.572209
## iter 190 value 280.357549
## iter 200 value 266.562766
## iter 210 value 254.535847
## iter 220 value 244.178157
## iter 230 value 237.643888
## iter 240 value 231.857390
## iter 250 value 224.345362
## iter 260 value 220.689994
## iter 270 value 218.606895
## iter 280 value 217.072419
## iter 290 value 216.532138
## iter 300 value 215.999574
## iter 310 value 215.285988
## iter 320 value 214.318708
## iter 330 value 212.310094
## iter 340 value 210.234280
## iter 350 value 208.193171
## iter 360 value 207.168231
## iter 370 value 206.143178
## iter 380 value 205.280567
## iter 390 value 204.495536
## iter 400 value 203.580698
## iter 410 value 202.896600
## iter 420 value 202.034853
## iter 430 value 200.754908
## iter 440 value 199.913948
## iter 450 value 199.467116
## iter 460 value 199.206448
## iter 470 value 198.699061
## iter 480 value 194.732367
## iter 490 value 191.657804
## iter 500 value 189.040110
## final value 189.040110
## stopped after 500 iterations
## # weights: 15
## initial value 1420233.231794
## iter 10 value 72824.933060
## iter 20 value 7686.083658
## iter 30 value 2585.826051
## iter 40 value 1889.052754
## iter 50 value 1818.810222
## iter 60 value 1769.759746
## iter 70 value 1759.660572
## iter 80 value 1758.303113
## iter 90 value 1754.949232
## iter 100 value 1754.033414
## iter 110 value 1749.794866
## iter 120 value 1748.930384
## iter 130 value 1747.485461
## iter 140 value 1741.655083
## iter 150 value 1737.890747
## iter 160 value 1737.380249
## iter 170 value 1734.990023
## iter 180 value 1730.467206
## iter 190 value 1701.748252
## iter 200 value 1657.638733
## iter 210 value 1611.231712
## iter 220 value 1347.596525
## iter 230 value 1247.001076
## iter 240 value 1220.440842
## iter 250 value 1187.254709
## iter 260 value 1180.289545
## iter 270 value 1180.142354
## iter 280 value 1179.820896
## iter 290 value 1179.351309
## iter 300 value 1179.318396
## iter 310 value 1179.104688
## iter 320 value 1178.443368
## iter 330 value 1178.217280
## iter 340 value 1177.976718
## iter 350 value 1177.483910
## iter 360 value 1177.145903
## iter 370 value 1176.872470
## iter 380 value 1175.771875
## iter 390 value 1174.936143
## iter 400 value 1174.020837
## iter 410 value 1172.296616
## iter 420 value 1171.803909
## iter 430 value 1171.710385
## iter 440 value 1171.630748
## final value 1171.626535
## converged
## # weights: 36
## initial value 1377478.536983
## iter 10 value 8245.875436
## iter 20 value 3072.328775
## iter 30 value 1894.546374
## iter 40 value 1383.894150
## iter 50 value 1149.882290
## iter 60 value 1086.575855
## iter 70 value 1041.940879
## iter 80 value 1037.713885
## iter 90 value 1028.718193
## iter 100 value 1013.551115
## iter 110 value 1000.828098
## iter 120 value 968.192258
## iter 130 value 948.528103
## iter 140 value 932.576946
## iter 150 value 913.320604
## iter 160 value 905.395123
## iter 170 value 898.727362
## iter 180 value 889.982531
## iter 190 value 886.226974
## iter 200 value 884.740083
## iter 210 value 883.939910
## iter 220 value 883.236821
## iter 230 value 883.144026
## iter 240 value 883.072724
## iter 250 value 882.818938
## iter 260 value 882.051527
## iter 270 value 881.412403
## iter 280 value 880.500545
## iter 290 value 879.603864
## iter 300 value 879.425685
## iter 310 value 879.402045
## iter 320 value 879.365995
## iter 330 value 879.335461
## iter 340 value 879.318525
## iter 350 value 879.267244
## iter 360 value 879.262811
## iter 370 value 879.218831
## iter 380 value 879.217606
## iter 390 value 879.212485
## iter 400 value 879.093072
## iter 410 value 879.062739
## iter 420 value 878.453998
## iter 430 value 876.135221
## iter 440 value 868.179758
## iter 450 value 862.467991
## iter 460 value 860.631476
## iter 470 value 859.901115
## iter 480 value 859.894583
## iter 490 value 859.882043
## iter 490 value 859.882041
## final value 859.882041
## converged
## # weights: 71
## initial value 1414148.933316
## iter 10 value 1639.009149
## iter 20 value 1119.523925
## iter 30 value 1018.934173
## iter 40 value 943.330056
## iter 50 value 898.298762
## iter 60 value 875.962676
## iter 70 value 852.757234
## iter 80 value 822.678789
## iter 90 value 786.678396
## iter 100 value 754.872652
## iter 110 value 726.064866
## iter 120 value 711.487305
## iter 130 value 696.580867
## iter 140 value 680.561011
## iter 150 value 671.881421
## iter 160 value 667.892477
## iter 170 value 659.283581
## iter 180 value 650.022477
## iter 190 value 641.495508
## iter 200 value 631.701145
## iter 210 value 607.242805
## iter 220 value 579.029365
## iter 230 value 567.176955
## iter 240 value 559.866760
## iter 250 value 556.780081
## iter 260 value 555.669176
## iter 270 value 554.578424
## iter 280 value 553.665928
## iter 290 value 553.381924
## iter 300 value 553.343726
## iter 310 value 553.298412
## iter 320 value 553.268264
## iter 330 value 553.192785
## iter 340 value 553.129840
## iter 350 value 552.115042
## iter 360 value 552.036909
## iter 370 value 550.997426
## iter 380 value 550.801109
## iter 390 value 550.775383
## final value 550.735265
## converged
## # weights: 106
## initial value 1453336.037855
## iter 10 value 1820.586956
## iter 20 value 1078.222267
## iter 30 value 940.487328
## iter 40 value 833.753838
## iter 50 value 749.525527
## iter 60 value 697.302406
## iter 70 value 664.434318
## iter 80 value 640.350711
## iter 90 value 623.931378
## iter 100 value 608.760205
## iter 110 value 591.108640
## iter 120 value 551.379667
## iter 130 value 512.613586
## iter 140 value 475.005600
## iter 150 value 459.523438
## iter 160 value 450.505368
## iter 170 value 441.622809
## iter 180 value 432.006703
## iter 190 value 424.805599
## iter 200 value 419.669779
## iter 210 value 414.660947
## iter 220 value 413.318250
## iter 230 value 412.877222
## iter 240 value 412.230969
## iter 250 value 410.796899
## iter 260 value 408.643973
## iter 270 value 406.082772
## iter 280 value 403.208333
## iter 290 value 400.556846
## iter 300 value 397.375323
## iter 310 value 392.567862
## iter 320 value 388.909083
## iter 330 value 386.790814
## iter 340 value 385.907383
## iter 350 value 382.876547
## iter 360 value 380.475740
## iter 370 value 379.114179
## iter 380 value 377.940263
## iter 390 value 377.480319
## iter 400 value 377.106174
## iter 410 value 376.539383
## iter 420 value 376.330141
## iter 430 value 376.276542
## iter 440 value 376.269503
## iter 450 value 376.262611
## iter 460 value 376.243786
## iter 470 value 376.206031
## iter 480 value 376.124653
## iter 490 value 375.977379
## iter 500 value 375.806386
## final value 375.806386
## stopped after 500 iterations
## # weights: 141
## initial value 1356386.438590
## iter 10 value 1506.888527
## iter 20 value 978.485610
## iter 30 value 826.714424
## iter 40 value 738.397106
## iter 50 value 667.866788
## iter 60 value 612.575851
## iter 70 value 573.927537
## iter 80 value 553.403119
## iter 90 value 526.342407
## iter 100 value 488.142545
## iter 110 value 454.814186
## iter 120 value 431.521530
## iter 130 value 420.199413
## iter 140 value 408.194922
## iter 150 value 393.920237
## iter 160 value 380.075578
## iter 170 value 362.301148
## iter 180 value 337.879537
## iter 190 value 319.092985
## iter 200 value 305.086712
## iter 210 value 289.436610
## iter 220 value 275.282778
## iter 230 value 263.088370
## iter 240 value 255.861135
## iter 250 value 250.041495
## iter 260 value 243.849243
## iter 270 value 236.090546
## iter 280 value 230.140358
## iter 290 value 227.886461
## iter 300 value 226.342697
## iter 310 value 224.425217
## iter 320 value 220.846661
## iter 330 value 217.265587
## iter 340 value 210.726473
## iter 350 value 205.568968
## iter 360 value 198.844698
## iter 370 value 191.602181
## iter 380 value 187.115342
## iter 390 value 183.725270
## iter 400 value 179.301109
## iter 410 value 176.980711
## iter 420 value 172.813455
## iter 430 value 169.147296
## iter 440 value 166.695214
## iter 450 value 165.032692
## iter 460 value 162.842445
## iter 470 value 161.621900
## iter 480 value 160.932510
## iter 490 value 160.140090
## iter 500 value 159.626919
## final value 159.626919
## stopped after 500 iterations
## # weights: 15
## initial value 1384763.883320
## iter 10 value 7709.613116
## iter 20 value 5823.399736
## iter 30 value 5774.511454
## iter 40 value 5430.634202
## iter 50 value 3748.385141
## iter 60 value 2172.464199
## iter 70 value 1835.834953
## iter 80 value 1797.941458
## iter 90 value 1761.069461
## iter 100 value 1755.193933
## iter 110 value 1754.084052
## iter 120 value 1753.378876
## iter 130 value 1752.814657
## iter 140 value 1752.357952
## final value 1752.356589
## converged
## # weights: 36
## initial value 1433335.448558
## iter 10 value 42664.911351
## iter 20 value 11963.604967
## iter 30 value 5739.942922
## iter 40 value 4903.331476
## iter 50 value 2780.652397
## iter 60 value 2318.589688
## iter 70 value 1958.132627
## iter 80 value 1795.713278
## iter 90 value 1706.002418
## iter 100 value 1544.410946
## iter 110 value 1534.591106
## iter 120 value 1533.024883
## iter 130 value 1493.273538
## iter 140 value 1473.025962
## iter 150 value 1470.725067
## iter 160 value 1469.965157
## iter 170 value 1465.845417
## iter 180 value 1463.578753
## iter 190 value 1463.555731
## iter 200 value 1463.485148
## iter 210 value 1463.013525
## iter 220 value 1461.948810
## iter 230 value 1461.276859
## iter 240 value 1460.677282
## iter 250 value 1460.545000
## iter 260 value 1460.499188
## iter 270 value 1460.494519
## iter 280 value 1460.468861
## final value 1460.468795
## converged
## # weights: 71
## initial value 1401681.506208
## iter 10 value 2854.215405
## iter 20 value 1487.814304
## iter 30 value 1201.871973
## iter 40 value 1024.282208
## iter 50 value 888.161231
## iter 60 value 803.317577
## iter 70 value 727.537760
## iter 80 value 689.000959
## iter 90 value 673.970894
## iter 100 value 657.883733
## iter 110 value 640.746142
## iter 120 value 628.300597
## iter 130 value 618.870330
## iter 140 value 615.201054
## iter 150 value 613.206542
## iter 160 value 612.776573
## iter 170 value 610.883169
## iter 180 value 607.158440
## iter 190 value 597.743822
## iter 200 value 588.247183
## iter 210 value 581.756431
## iter 220 value 575.689537
## iter 230 value 569.999619
## iter 240 value 567.308965
## iter 250 value 564.315996
## iter 260 value 562.268010
## iter 270 value 561.902758
## iter 280 value 561.832860
## iter 290 value 561.814868
## iter 300 value 561.811727
## iter 310 value 561.808621
## iter 320 value 561.805016
## iter 330 value 561.797760
## iter 340 value 561.782765
## iter 350 value 561.725533
## iter 360 value 561.664280
## iter 370 value 561.563971
## iter 380 value 561.471524
## iter 390 value 561.393688
## iter 400 value 561.349385
## iter 410 value 561.328545
## iter 420 value 561.311099
## iter 430 value 561.273777
## iter 440 value 561.272772
## iter 450 value 561.271119
## iter 460 value 561.267529
## iter 470 value 561.260158
## iter 480 value 561.242793
## iter 490 value 561.214571
## iter 500 value 561.203714
## final value 561.203714
## stopped after 500 iterations
## # weights: 106
## initial value 1449116.096592
## iter 10 value 3524.168720
## iter 20 value 1706.056434
## iter 30 value 1175.241569
## iter 40 value 949.164296
## iter 50 value 800.218551
## iter 60 value 714.812143
## iter 70 value 654.456917
## iter 80 value 604.845858
## iter 90 value 575.604733
## iter 100 value 552.360200
## iter 110 value 520.383974
## iter 120 value 496.063929
## iter 130 value 481.476909
## iter 140 value 475.005205
## iter 150 value 470.704458
## iter 160 value 463.748575
## iter 170 value 458.204308
## iter 180 value 452.466570
## iter 190 value 448.152740
## iter 200 value 444.498272
## iter 210 value 441.570824
## iter 220 value 440.726666
## iter 230 value 439.303303
## iter 240 value 437.816046
## iter 250 value 435.474563
## iter 260 value 432.000325
## iter 270 value 427.115436
## iter 280 value 425.128583
## iter 290 value 423.514197
## iter 300 value 421.094465
## iter 310 value 418.586113
## iter 320 value 416.935557
## iter 330 value 415.238200
## iter 340 value 413.329691
## iter 350 value 411.762591
## iter 360 value 410.146748
## iter 370 value 408.773403
## iter 380 value 408.274991
## iter 390 value 407.680032
## iter 400 value 407.150610
## iter 410 value 406.649517
## iter 420 value 405.060350
## iter 430 value 403.674625
## iter 440 value 403.558472
## iter 450 value 403.372627
## iter 460 value 403.161878
## iter 470 value 402.866513
## iter 480 value 402.580088
## iter 490 value 402.212321
## iter 500 value 401.966677
## final value 401.966677
## stopped after 500 iterations
## # weights: 141
## initial value 1462307.700241
## iter 10 value 1476.895771
## iter 20 value 1063.115693
## iter 30 value 921.095303
## iter 40 value 771.355034
## iter 50 value 671.327010
## iter 60 value 613.877943
## iter 70 value 569.649365
## iter 80 value 499.171489
## iter 90 value 436.780669
## iter 100 value 390.221767
## iter 110 value 363.031769
## iter 120 value 349.958291
## iter 130 value 338.693274
## iter 140 value 323.473449
## iter 150 value 306.922440
## iter 160 value 296.666199
## iter 170 value 288.678084
## iter 180 value 277.862885
## iter 190 value 267.448201
## iter 200 value 260.127440
## iter 210 value 253.774820
## iter 220 value 249.223254
## iter 230 value 245.940086
## iter 240 value 239.333908
## iter 250 value 233.388802
## iter 260 value 227.590944
## iter 270 value 222.207832
## iter 280 value 215.747398
## iter 290 value 213.112564
## iter 300 value 211.772907
## iter 310 value 209.216559
## iter 320 value 206.560042
## iter 330 value 201.043360
## iter 340 value 195.686972
## iter 350 value 193.049015
## iter 360 value 191.195361
## iter 370 value 189.194753
## iter 380 value 185.021034
## iter 390 value 179.175786
## iter 400 value 174.751843
## iter 410 value 168.371510
## iter 420 value 163.089337
## iter 430 value 160.641851
## iter 440 value 157.782823
## iter 450 value 155.574119
## iter 460 value 153.662207
## iter 470 value 152.531811
## iter 480 value 152.035849
## iter 490 value 151.848877
## iter 500 value 151.620446
## final value 151.620446
## stopped after 500 iterations
## # weights: 15
## initial value 1381310.525640
## iter 10 value 184370.825597
## iter 20 value 15721.272210
## iter 30 value 11172.698691
## iter 40 value 11001.786749
## final value 11001.231031
## converged
## # weights: 36
## initial value 1410413.643841
## iter 10 value 32670.958696
## iter 20 value 5985.176422
## iter 30 value 4062.968108
## iter 40 value 3603.317766
## iter 50 value 2455.779456
## iter 60 value 2038.166763
## iter 70 value 2015.887271
## iter 80 value 2013.609241
## iter 90 value 2010.928127
## iter 100 value 1916.382925
## iter 110 value 1911.572087
## iter 120 value 1907.030525
## iter 130 value 1720.279669
## iter 140 value 1540.677944
## iter 150 value 1462.763899
## iter 160 value 1437.279325
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## iter 470 value 1099.145668
## iter 480 value 1099.069776
## iter 490 value 1099.052372
## iter 500 value 1098.988013
## final value 1098.988013
## stopped after 500 iterations
## # weights: 71
## initial value 1394447.666814
## iter 10 value 2826.643814
## iter 20 value 1833.045391
## iter 30 value 1441.059567
## iter 40 value 1186.198686
## iter 50 value 1122.980105
## iter 60 value 990.774449
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## iter 400 value 664.268534
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## iter 470 value 663.719206
## iter 480 value 663.635248
## iter 490 value 663.575065
## iter 500 value 663.560773
## final value 663.560773
## stopped after 500 iterations
## # weights: 106
## initial value 1377845.062161
## iter 10 value 1451.872650
## iter 20 value 1048.465775
## iter 30 value 941.434261
## iter 40 value 833.039762
## iter 50 value 790.930781
## iter 60 value 732.391620
## iter 70 value 671.843922
## iter 80 value 613.133013
## iter 90 value 576.317084
## iter 100 value 537.065821
## iter 110 value 509.144136
## iter 120 value 489.510638
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## iter 180 value 404.786707
## iter 190 value 395.269877
## iter 200 value 387.668751
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## iter 250 value 374.045130
## iter 260 value 370.042030
## iter 270 value 366.786100
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## iter 290 value 355.554972
## iter 300 value 351.229886
## iter 310 value 343.293849
## iter 320 value 337.288438
## iter 330 value 330.580207
## iter 340 value 319.617001
## iter 350 value 309.011403
## iter 360 value 302.039542
## iter 370 value 293.588705
## iter 380 value 287.556545
## iter 390 value 283.258416
## iter 400 value 282.079198
## iter 410 value 281.582408
## iter 420 value 279.368843
## iter 430 value 277.348277
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## iter 450 value 276.994981
## iter 460 value 276.367440
## iter 470 value 275.532852
## iter 480 value 274.567877
## iter 490 value 273.641980
## iter 500 value 273.332181
## final value 273.332181
## stopped after 500 iterations
## # weights: 141
## initial value 1409418.855055
## iter 10 value 1497.388267
## iter 20 value 1072.109825
## iter 30 value 885.309188
## iter 40 value 798.911587
## iter 50 value 741.490090
## iter 60 value 689.951782
## iter 70 value 607.433960
## iter 80 value 561.055202
## iter 90 value 505.002556
## iter 100 value 476.023076
## iter 110 value 439.591960
## iter 120 value 417.373204
## iter 130 value 404.256262
## iter 140 value 391.358798
## iter 150 value 373.322501
## iter 160 value 362.422480
## iter 170 value 356.510595
## iter 180 value 350.837281
## iter 190 value 347.196708
## iter 200 value 340.311211
## iter 210 value 323.330128
## iter 220 value 309.196291
## iter 230 value 295.520065
## iter 240 value 287.155113
## iter 250 value 282.687722
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## iter 270 value 267.122993
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## iter 290 value 250.295892
## iter 300 value 248.399695
## iter 310 value 245.078835
## iter 320 value 241.816856
## iter 330 value 239.177883
## iter 340 value 237.054524
## iter 350 value 234.528651
## iter 360 value 230.029685
## iter 370 value 226.219806
## iter 380 value 218.989053
## iter 390 value 208.629102
## iter 400 value 201.731775
## iter 410 value 196.280224
## iter 420 value 191.298374
## iter 430 value 184.559472
## iter 440 value 177.169356
## iter 450 value 172.989802
## iter 460 value 171.461455
## iter 470 value 168.946556
## iter 480 value 167.724256
## iter 490 value 166.510564
## iter 500 value 165.788840
## final value 165.788840
## stopped after 500 iterations
## # weights: 15
## initial value 1389940.740895
## iter 10 value 9957.104566
## iter 20 value 6811.969095
## iter 30 value 4157.058720
## iter 40 value 3859.551176
## iter 50 value 3506.945601
## iter 60 value 2518.548077
## iter 70 value 2230.526366
## iter 80 value 2166.642866
## iter 90 value 2125.440613
## iter 100 value 1890.545496
## iter 110 value 1750.353042
## iter 120 value 1706.437328
## iter 130 value 1678.778925
## iter 140 value 1540.241014
## iter 150 value 1503.841040
## iter 160 value 1493.883499
## iter 170 value 1479.923571
## iter 180 value 1479.266851
## iter 190 value 1479.260791
## final value 1479.259761
## converged
## # weights: 36
## initial value 1377192.040010
## iter 10 value 8206.928004
## iter 20 value 4747.656249
## iter 30 value 3849.258071
## iter 40 value 3335.763157
## iter 50 value 2781.027802
## iter 60 value 2148.715607
## iter 70 value 1643.980617
## iter 80 value 1601.475291
## iter 90 value 1448.126870
## iter 100 value 1315.531294
## iter 110 value 1268.228531
## iter 120 value 1247.851899
## iter 130 value 1220.169504
## iter 140 value 1211.048350
## iter 150 value 1202.802349
## iter 160 value 1200.585349
## iter 170 value 1194.113065
## iter 180 value 1191.520538
## iter 190 value 1191.446954
## iter 190 value 1191.446948
## iter 190 value 1191.446948
## final value 1191.446948
## converged
## # weights: 71
## initial value 1421186.199171
## iter 10 value 2270.143658
## iter 20 value 1287.836057
## iter 30 value 1117.254077
## iter 40 value 1061.520496
## iter 50 value 1011.991890
## iter 60 value 985.568951
## iter 70 value 965.506736
## iter 80 value 936.931759
## iter 90 value 921.505914
## iter 100 value 915.358551
## iter 110 value 908.873493
## iter 120 value 899.396854
## iter 130 value 890.312828
## iter 140 value 885.065368
## iter 150 value 880.378233
## iter 160 value 877.696333
## iter 170 value 872.260532
## iter 180 value 868.675828
## iter 190 value 866.533102
## iter 200 value 865.245994
## iter 210 value 862.099783
## iter 220 value 852.971274
## iter 230 value 842.956800
## iter 240 value 841.777857
## iter 250 value 841.682616
## iter 260 value 841.039158
## iter 270 value 838.514937
## iter 280 value 837.744798
## iter 290 value 837.572287
## iter 300 value 837.569315
## iter 310 value 837.567939
## iter 320 value 837.566484
## iter 330 value 837.565088
## iter 340 value 837.564339
## iter 350 value 837.563990
## final value 837.563968
## converged
## # weights: 106
## initial value 1458357.223224
## iter 10 value 2650.438708
## iter 20 value 1390.316058
## iter 30 value 1179.426958
## iter 40 value 1078.408409
## iter 50 value 969.755398
## iter 60 value 908.456597
## iter 70 value 856.850537
## iter 80 value 831.129298
## iter 90 value 806.745293
## iter 100 value 788.407594
## iter 110 value 765.438546
## iter 120 value 731.292665
## iter 130 value 714.241555
## iter 140 value 704.463430
## iter 150 value 699.519538
## iter 160 value 697.142361
## iter 170 value 695.026594
## iter 180 value 693.583258
## iter 190 value 692.698833
## iter 200 value 691.377784
## iter 210 value 690.068715
## iter 220 value 689.563755
## iter 230 value 688.624554
## iter 240 value 687.347886
## iter 250 value 686.883236
## iter 260 value 686.629247
## iter 270 value 686.160763
## iter 280 value 682.914465
## iter 290 value 677.866397
## iter 300 value 673.668326
## iter 310 value 669.345711
## iter 320 value 666.599214
## iter 330 value 665.535321
## iter 340 value 665.032655
## iter 350 value 664.895299
## iter 360 value 664.889353
## iter 370 value 664.888947
## final value 664.888734
## converged
## # weights: 141
## initial value 1482179.119857
## iter 10 value 1589.200992
## iter 20 value 1209.800561
## iter 30 value 1025.775127
## iter 40 value 932.003206
## iter 50 value 851.903898
## iter 60 value 778.138444
## iter 70 value 743.885754
## iter 80 value 706.370840
## iter 90 value 683.082198
## iter 100 value 662.672223
## iter 110 value 648.697112
## iter 120 value 640.045876
## iter 130 value 631.821129
## iter 140 value 623.775969
## iter 150 value 616.631496
## iter 160 value 612.598898
## iter 170 value 609.317352
## iter 180 value 604.708204
## iter 190 value 599.811574
## iter 200 value 593.373256
## iter 210 value 582.025036
## iter 220 value 575.090261
## iter 230 value 570.716678
## iter 240 value 565.288739
## iter 250 value 556.320603
## iter 260 value 550.199671
## iter 270 value 545.737524
## iter 280 value 542.086459
## iter 290 value 540.826504
## iter 300 value 539.078972
## iter 310 value 536.705876
## iter 320 value 535.415073
## iter 330 value 534.843660
## iter 340 value 534.329765
## iter 350 value 533.836938
## iter 360 value 532.414704
## iter 370 value 528.686253
## iter 380 value 526.045872
## iter 390 value 525.100521
## iter 400 value 523.919094
## iter 410 value 522.178684
## iter 420 value 521.334508
## iter 430 value 520.923906
## iter 440 value 520.686507
## iter 450 value 520.445265
## iter 460 value 520.287517
## iter 470 value 520.135982
## iter 480 value 519.623294
## iter 490 value 518.316619
## iter 500 value 517.477285
## final value 517.477285
## stopped after 500 iterations
## # weights: 15
## initial value 1460601.304015
## iter 10 value 10845.612278
## iter 20 value 5993.198342
## iter 30 value 3433.962091
## iter 40 value 2928.356814
## iter 50 value 1820.880171
## iter 60 value 1357.694256
## iter 70 value 1300.219164
## iter 80 value 1240.196139
## iter 90 value 1222.548569
## iter 100 value 1216.767215
## iter 110 value 1212.267401
## iter 120 value 1205.911100
## iter 130 value 1203.921042
## iter 140 value 1203.405736
## iter 150 value 1202.597981
## final value 1202.588125
## converged
## # weights: 36
## initial value 1386049.109483
## iter 10 value 5560.070422
## iter 20 value 3308.235498
## iter 30 value 2723.605917
## iter 40 value 2390.973078
## iter 50 value 2053.048943
## iter 60 value 1838.203934
## iter 70 value 1560.640776
## iter 80 value 1382.781268
## iter 90 value 1241.549306
## iter 100 value 1209.984476
## iter 110 value 1189.113345
## iter 120 value 1171.062287
## iter 130 value 1162.547225
## iter 140 value 1135.473078
## iter 150 value 1061.637193
## iter 160 value 1037.205400
## iter 170 value 980.467976
## iter 180 value 945.452252
## iter 190 value 936.328078
## iter 200 value 936.240271
## iter 210 value 936.092762
## iter 220 value 935.510123
## iter 230 value 934.805458
## iter 240 value 934.766385
## iter 250 value 934.762276
## iter 250 value 934.762267
## iter 250 value 934.762267
## final value 934.762267
## converged
## # weights: 71
## initial value 1423258.262944
## iter 10 value 2920.279031
## iter 20 value 1496.410974
## iter 30 value 1068.457708
## iter 40 value 965.241150
## iter 50 value 916.486679
## iter 60 value 871.761186
## iter 70 value 839.288220
## iter 80 value 818.364130
## iter 90 value 806.435723
## iter 100 value 792.996491
## iter 110 value 779.629277
## iter 120 value 772.532256
## iter 130 value 763.287808
## iter 140 value 754.114248
## iter 150 value 750.249048
## iter 160 value 741.709307
## iter 170 value 727.827930
## iter 180 value 714.500792
## iter 190 value 706.468660
## iter 200 value 696.559419
## iter 210 value 683.287811
## iter 220 value 671.861897
## iter 230 value 666.893207
## iter 240 value 664.085524
## iter 250 value 663.631153
## iter 260 value 663.366670
## iter 270 value 663.123084
## iter 280 value 662.988047
## iter 290 value 662.844573
## iter 300 value 662.825733
## iter 310 value 662.781841
## iter 320 value 662.741437
## iter 330 value 662.552569
## iter 340 value 662.234814
## iter 350 value 661.915456
## iter 360 value 661.731221
## iter 370 value 661.539484
## iter 380 value 661.484411
## iter 390 value 661.443712
## iter 400 value 661.419390
## iter 410 value 660.847146
## iter 420 value 660.422768
## iter 430 value 660.224488
## iter 440 value 660.207339
## iter 450 value 660.152524
## iter 460 value 659.955246
## iter 470 value 659.095394
## iter 480 value 658.711023
## iter 490 value 658.606856
## iter 500 value 658.477546
## final value 658.477546
## stopped after 500 iterations
## # weights: 106
## initial value 1347528.453424
## iter 10 value 1382.944585
## iter 20 value 1145.173218
## iter 30 value 955.624448
## iter 40 value 887.371954
## iter 50 value 822.553812
## iter 60 value 733.526786
## iter 70 value 685.481743
## iter 80 value 627.381122
## iter 90 value 588.142681
## iter 100 value 550.496479
## iter 110 value 524.180413
## iter 120 value 516.558335
## iter 130 value 506.573196
## iter 140 value 496.584657
## iter 150 value 480.199127
## iter 160 value 469.275325
## iter 170 value 459.163096
## iter 180 value 450.583451
## iter 190 value 436.234000
## iter 200 value 429.628273
## iter 210 value 426.342400
## iter 220 value 423.912178
## iter 230 value 420.848633
## iter 240 value 415.754128
## iter 250 value 404.584755
## iter 260 value 395.758275
## iter 270 value 390.693751
## iter 280 value 384.542451
## iter 290 value 379.733840
## iter 300 value 375.824221
## iter 310 value 373.130574
## iter 320 value 371.511155
## iter 330 value 370.682927
## iter 340 value 370.272343
## iter 350 value 370.227577
## iter 360 value 370.217587
## iter 370 value 370.213776
## iter 380 value 370.212131
## iter 390 value 370.211417
## iter 400 value 370.211117
## final value 370.211080
## converged
## # weights: 141
## initial value 1447720.435724
## iter 10 value 1380.455883
## iter 20 value 1096.680256
## iter 30 value 943.796371
## iter 40 value 819.854950
## iter 50 value 730.111775
## iter 60 value 624.753192
## iter 70 value 561.658234
## iter 80 value 500.622888
## iter 90 value 449.636978
## iter 100 value 408.997137
## iter 110 value 375.047197
## iter 120 value 355.358987
## iter 130 value 340.769838
## iter 140 value 324.842520
## iter 150 value 304.097478
## iter 160 value 293.421824
## iter 170 value 284.906374
## iter 180 value 280.754635
## iter 190 value 276.633769
## iter 200 value 272.420532
## iter 210 value 268.583599
## iter 220 value 265.071700
## iter 230 value 262.727775
## iter 240 value 260.709253
## iter 250 value 256.814446
## iter 260 value 251.884876
## iter 270 value 248.764738
## iter 280 value 246.287595
## iter 290 value 245.295705
## iter 300 value 244.629137
## iter 310 value 243.319949
## iter 320 value 242.327345
## iter 330 value 240.725145
## iter 340 value 237.717675
## iter 350 value 235.261471
## iter 360 value 231.683225
## iter 370 value 228.473136
## iter 380 value 226.201439
## iter 390 value 224.920342
## iter 400 value 223.886913
## iter 410 value 223.067058
## iter 420 value 222.064956
## iter 430 value 221.605381
## iter 440 value 220.120005
## iter 450 value 214.172930
## iter 460 value 212.879307
## iter 470 value 212.233874
## iter 480 value 211.984394
## iter 490 value 211.842031
## iter 500 value 211.770536
## final value 211.770536
## stopped after 500 iterations
## # weights: 15
## initial value 1395117.114379
## iter 10 value 5573.911464
## iter 20 value 5373.943474
## iter 30 value 5082.060489
## iter 40 value 4693.537585
## iter 50 value 4246.704092
## iter 60 value 3482.209754
## iter 70 value 1806.018618
## iter 80 value 1596.093289
## iter 90 value 1280.091187
## iter 100 value 1231.069426
## iter 110 value 1230.468305
## iter 120 value 1216.221651
## iter 130 value 1205.715527
## iter 140 value 1205.344869
## iter 150 value 1202.787255
## iter 160 value 1199.790329
## iter 170 value 1199.549015
## iter 180 value 1198.932665
## iter 190 value 1198.013387
## iter 200 value 1197.931163
## iter 210 value 1197.892756
## iter 220 value 1197.756518
## final value 1197.754722
## converged
## # weights: 36
## initial value 1379593.852359
## iter 10 value 6612.326334
## iter 20 value 3379.828877
## iter 30 value 2627.730222
## iter 40 value 2054.781541
## iter 50 value 1751.934847
## iter 60 value 1614.494965
## iter 70 value 1550.486507
## iter 80 value 1499.603219
## iter 90 value 1450.359980
## iter 100 value 1400.779627
## iter 110 value 1237.642239
## iter 120 value 1166.209526
## iter 130 value 1145.249601
## iter 140 value 1143.104619
## iter 150 value 1142.209020
## iter 160 value 1141.708577
## iter 170 value 1141.573814
## iter 180 value 1141.452003
## iter 190 value 1141.244262
## iter 200 value 1140.966160
## iter 210 value 1140.888086
## iter 220 value 1140.797805
## iter 230 value 1140.739534
## final value 1140.725278
## converged
## # weights: 71
## initial value 1408035.109725
## iter 10 value 5552.077174
## iter 20 value 2361.826117
## iter 30 value 1581.244059
## iter 40 value 1310.866001
## iter 50 value 1068.153027
## iter 60 value 993.523878
## iter 70 value 924.454721
## iter 80 value 903.824762
## iter 90 value 858.890838
## iter 100 value 808.871928
## iter 110 value 787.261738
## iter 120 value 772.834143
## iter 130 value 769.749350
## iter 140 value 767.665152
## iter 150 value 767.174416
## iter 160 value 767.105481
## iter 170 value 766.938027
## iter 180 value 766.691497
## iter 190 value 765.138805
## iter 200 value 763.344142
## iter 210 value 760.543632
## iter 220 value 744.887509
## iter 230 value 718.439613
## iter 240 value 672.402077
## iter 250 value 656.493841
## iter 260 value 653.648861
## iter 270 value 653.042136
## iter 280 value 651.476944
## iter 290 value 649.949663
## iter 300 value 645.932168
## iter 310 value 640.642831
## iter 320 value 630.178241
## iter 330 value 617.020363
## iter 340 value 604.642938
## iter 350 value 599.532930
## iter 360 value 597.520000
## iter 370 value 596.578439
## iter 380 value 594.545620
## iter 390 value 593.481215
## iter 400 value 592.845240
## iter 410 value 592.726385
## iter 420 value 592.595018
## iter 430 value 592.119317
## iter 440 value 587.604893
## iter 450 value 585.347784
## iter 460 value 573.218551
## iter 470 value 566.069619
## iter 480 value 559.791449
## iter 490 value 556.161288
## iter 500 value 555.700304
## final value 555.700304
## stopped after 500 iterations
## # weights: 106
## initial value 1481931.662579
## iter 10 value 1788.057403
## iter 20 value 1144.757178
## iter 30 value 947.477339
## iter 40 value 826.454253
## iter 50 value 753.277889
## iter 60 value 689.485542
## iter 70 value 632.086125
## iter 80 value 589.056226
## iter 90 value 555.001617
## iter 100 value 527.257388
## iter 110 value 508.216162
## iter 120 value 485.008446
## iter 130 value 460.735692
## iter 140 value 449.900788
## iter 150 value 438.661894
## iter 160 value 427.993808
## iter 170 value 420.415600
## iter 180 value 409.580102
## iter 190 value 395.557814
## iter 200 value 389.355945
## iter 210 value 384.194798
## iter 220 value 380.985031
## iter 230 value 376.819261
## iter 240 value 371.607751
## iter 250 value 368.001113
## iter 260 value 362.403183
## iter 270 value 355.963157
## iter 280 value 347.533128
## iter 290 value 339.272426
## iter 300 value 329.717481
## iter 310 value 325.812621
## iter 320 value 323.397915
## iter 330 value 320.106031
## iter 340 value 317.850935
## iter 350 value 317.012738
## iter 360 value 316.583239
## iter 370 value 316.011412
## iter 380 value 315.525706
## iter 390 value 313.647982
## iter 400 value 311.487533
## iter 410 value 310.246526
## iter 420 value 309.118465
## iter 430 value 308.544993
## iter 440 value 308.489135
## iter 450 value 308.432721
## iter 460 value 308.382531
## iter 470 value 308.347906
## iter 480 value 308.281954
## iter 490 value 308.123232
## iter 500 value 307.536282
## final value 307.536282
## stopped after 500 iterations
## # weights: 141
## initial value 1412261.758369
## iter 10 value 1688.400932
## iter 20 value 1153.671944
## iter 30 value 975.153052
## iter 40 value 840.663132
## iter 50 value 677.691954
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## iter 70 value 533.126764
## iter 80 value 452.181835
## iter 90 value 431.568611
## iter 100 value 410.775856
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## iter 150 value 338.250356
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## iter 170 value 309.828655
## iter 180 value 291.820143
## iter 190 value 269.817536
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## iter 220 value 236.868028
## iter 230 value 231.522319
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## iter 300 value 207.024516
## iter 310 value 205.501769
## iter 320 value 204.234290
## iter 330 value 202.447109
## iter 340 value 200.343652
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## iter 360 value 194.332959
## iter 370 value 191.326479
## iter 380 value 187.322453
## iter 390 value 183.707182
## iter 400 value 179.843003
## iter 410 value 177.723711
## iter 420 value 174.476939
## iter 430 value 172.104428
## iter 440 value 170.379525
## iter 450 value 168.322601
## iter 460 value 167.024371
## iter 470 value 165.682621
## iter 480 value 165.400846
## iter 490 value 164.938005
## iter 500 value 163.858923
## final value 163.858923
## stopped after 500 iterations
## # weights: 15
## initial value 1408337.019516
## iter 10 value 6824.951014
## iter 20 value 2344.949372
## iter 30 value 1616.638683
## iter 40 value 1599.129596
## iter 50 value 1539.351606
## iter 60 value 1509.819385
## iter 70 value 1504.455454
## iter 80 value 1498.605015
## iter 90 value 1484.554769
## iter 100 value 1480.187549
## iter 110 value 1477.959313
## iter 120 value 1472.202352
## iter 130 value 1471.664487
## iter 140 value 1471.418285
## iter 150 value 1469.860182
## iter 160 value 1469.129222
## iter 170 value 1468.587170
## iter 180 value 1466.945421
## iter 190 value 1466.622794
## iter 200 value 1466.582863
## iter 210 value 1466.031611
## iter 220 value 1465.579691
## iter 230 value 1465.459074
## iter 240 value 1464.488454
## iter 250 value 1464.221267
## iter 260 value 1464.217676
## final value 1464.217371
## converged
## # weights: 36
## initial value 1425167.244067
## iter 10 value 22944.644977
## iter 20 value 15437.894358
## iter 30 value 8921.251755
## iter 40 value 5121.262438
## iter 50 value 2495.995549
## iter 60 value 2301.895436
## iter 70 value 2110.763849
## iter 80 value 1883.726124
## iter 90 value 1723.570853
## iter 100 value 1618.978671
## iter 110 value 1482.745147
## iter 120 value 1451.631692
## iter 130 value 1441.221251
## iter 140 value 1428.944816
## iter 150 value 1426.826997
## iter 160 value 1424.245625
## iter 170 value 1424.077828
## iter 180 value 1422.391008
## iter 190 value 1418.410627
## iter 200 value 1416.332461
## iter 210 value 1413.508181
## iter 220 value 1411.720369
## iter 230 value 1410.047818
## final value 1410.024497
## converged
## # weights: 71
## initial value 1402787.659337
## iter 10 value 2139.335295
## iter 20 value 1233.339988
## iter 30 value 1102.449878
## iter 40 value 1003.989120
## iter 50 value 952.536592
## iter 60 value 907.688488
## iter 70 value 843.327823
## iter 80 value 818.014758
## iter 90 value 790.047569
## iter 100 value 770.554791
## iter 110 value 763.038802
## iter 120 value 757.118456
## iter 130 value 752.237938
## iter 140 value 746.107912
## iter 150 value 744.166768
## iter 160 value 742.454876
## iter 170 value 740.125753
## iter 180 value 736.870311
## iter 190 value 733.288067
## iter 200 value 721.830832
## iter 210 value 699.014664
## iter 220 value 683.134763
## iter 230 value 671.245587
## iter 240 value 658.528954
## iter 250 value 645.079206
## iter 260 value 635.997893
## iter 270 value 631.462283
## iter 280 value 627.255604
## iter 290 value 625.367804
## iter 300 value 624.541489
## iter 310 value 623.155179
## iter 320 value 619.368524
## iter 330 value 615.042497
## iter 340 value 609.505450
## iter 350 value 604.553953
## iter 360 value 601.665413
## iter 370 value 598.974049
## iter 380 value 597.040326
## iter 390 value 596.251802
## iter 400 value 595.469277
## iter 410 value 594.156251
## iter 420 value 589.519005
## iter 430 value 582.757583
## iter 440 value 580.127772
## iter 450 value 578.836864
## iter 460 value 576.068673
## iter 470 value 569.564480
## iter 480 value 565.064446
## iter 490 value 562.773166
## iter 500 value 561.997817
## final value 561.997817
## stopped after 500 iterations
## # weights: 106
## initial value 1423378.459551
## iter 10 value 1377.074981
## iter 20 value 1097.183045
## iter 30 value 959.382278
## iter 40 value 845.301732
## iter 50 value 754.050693
## iter 60 value 688.431840
## iter 70 value 629.499535
## iter 80 value 559.324227
## iter 90 value 520.294048
## iter 100 value 498.700802
## iter 110 value 471.483228
## iter 120 value 452.152360
## iter 130 value 429.687664
## iter 140 value 410.093096
## iter 150 value 399.368782
## iter 160 value 392.324369
## iter 170 value 388.206879
## iter 180 value 383.876188
## iter 190 value 379.172586
## iter 200 value 376.459270
## iter 210 value 373.542895
## iter 220 value 371.709141
## iter 230 value 370.683866
## iter 240 value 368.439784
## iter 250 value 365.603425
## iter 260 value 360.484459
## iter 270 value 353.188138
## iter 280 value 346.364478
## iter 290 value 334.873859
## iter 300 value 328.001820
## iter 310 value 323.700707
## iter 320 value 317.134865
## iter 330 value 311.445542
## iter 340 value 308.125513
## iter 350 value 306.012470
## iter 360 value 303.351582
## iter 370 value 299.955389
## iter 380 value 294.867113
## iter 390 value 292.039991
## iter 400 value 288.777171
## iter 410 value 285.387811
## iter 420 value 284.543460
## iter 430 value 283.872564
## iter 440 value 283.796577
## iter 450 value 283.740394
## iter 460 value 283.683711
## iter 470 value 283.632376
## iter 480 value 283.563969
## iter 490 value 283.456389
## iter 500 value 283.290178
## final value 283.290178
## stopped after 500 iterations
## # weights: 141
## initial value 1454681.732369
## iter 10 value 3136.383843
## iter 20 value 1468.363303
## iter 30 value 1147.435707
## iter 40 value 1020.054075
## iter 50 value 918.211394
## iter 60 value 879.660817
## iter 70 value 841.471708
## iter 80 value 773.037328
## iter 90 value 706.920798
## iter 100 value 675.449155
## iter 110 value 658.913148
## iter 120 value 623.836854
## iter 130 value 580.568717
## iter 140 value 555.299135
## iter 150 value 536.754261
## iter 160 value 506.529893
## iter 170 value 475.020203
## iter 180 value 439.714266
## iter 190 value 419.342574
## iter 200 value 408.697825
## iter 210 value 395.369381
## iter 220 value 372.170533
## iter 230 value 363.248617
## iter 240 value 353.452445
## iter 250 value 346.552086
## iter 260 value 336.152194
## iter 270 value 331.085999
## iter 280 value 327.249263
## iter 290 value 325.567630
## iter 300 value 325.130344
## iter 310 value 324.259470
## iter 320 value 322.754446
## iter 330 value 320.357736
## iter 340 value 317.334262
## iter 350 value 312.517305
## iter 360 value 303.374180
## iter 370 value 293.179202
## iter 380 value 287.624872
## iter 390 value 280.916699
## iter 400 value 271.533159
## iter 410 value 267.025220
## iter 420 value 263.675665
## iter 430 value 261.402399
## iter 440 value 259.359348
## iter 450 value 258.196668
## iter 460 value 257.619550
## iter 470 value 256.540648
## iter 480 value 254.214670
## iter 490 value 252.590476
## iter 500 value 251.335811
## final value 251.335811
## stopped after 500 iterations
## # weights: 15
## initial value 1402695.316282
## iter 10 value 6380.352761
## iter 20 value 6002.326924
## iter 30 value 5980.748924
## iter 40 value 5391.519870
## iter 50 value 2797.764418
## iter 60 value 1899.642602
## iter 70 value 1805.568552
## iter 80 value 1743.800828
## iter 90 value 1733.607811
## iter 100 value 1730.180347
## iter 110 value 1725.973992
## iter 120 value 1723.985045
## iter 130 value 1723.667320
## iter 140 value 1721.284011
## iter 150 value 1718.532893
## iter 160 value 1716.080746
## iter 170 value 1715.355982
## final value 1715.354998
## converged
## # weights: 36
## initial value 1402115.490621
## iter 10 value 37457.283603
## iter 20 value 4746.373495
## iter 30 value 4508.758656
## iter 40 value 4477.381341
## iter 50 value 4469.324491
## iter 60 value 4469.159192
## iter 70 value 4467.720435
## iter 80 value 4467.398012
## iter 90 value 4466.614499
## iter 100 value 4463.567578
## iter 110 value 4459.324481
## iter 120 value 4441.872973
## iter 130 value 4391.902141
## iter 140 value 4269.244472
## iter 150 value 3806.950846
## iter 160 value 2855.690192
## iter 170 value 1995.006446
## iter 180 value 1830.326712
## iter 190 value 1766.109259
## iter 200 value 1735.490826
## iter 210 value 1720.479163
## iter 220 value 1691.708398
## iter 230 value 1688.442968
## iter 240 value 1684.053231
## iter 250 value 1660.984423
## iter 260 value 1637.962624
## iter 270 value 1633.619952
## iter 280 value 1621.569095
## iter 290 value 1601.876626
## iter 300 value 1563.497877
## iter 310 value 1544.059958
## iter 320 value 1540.367313
## iter 330 value 1537.667897
## iter 340 value 1536.758936
## iter 350 value 1531.208435
## iter 360 value 1527.663359
## iter 370 value 1507.376364
## iter 380 value 1506.485420
## iter 390 value 1505.832306
## iter 400 value 1505.045965
## iter 410 value 1504.275794
## iter 420 value 1503.730055
## iter 430 value 1503.236974
## iter 440 value 1502.737656
## iter 450 value 1502.712578
## iter 450 value 1502.712574
## final value 1502.712092
## converged
## # weights: 71
## initial value 1393289.411761
## iter 10 value 4715.848397
## iter 20 value 2106.483288
## iter 30 value 1625.924165
## iter 40 value 1352.253325
## iter 50 value 1080.157973
## iter 60 value 947.041363
## iter 70 value 791.249133
## iter 80 value 712.079736
## iter 90 value 678.361034
## iter 100 value 664.725343
## iter 110 value 656.667570
## iter 120 value 649.704592
## iter 130 value 645.387812
## iter 140 value 636.312946
## iter 150 value 632.996933
## iter 160 value 631.598238
## iter 170 value 627.059316
## iter 180 value 620.045257
## iter 190 value 613.328928
## iter 200 value 593.534422
## iter 210 value 581.817028
## iter 220 value 571.205139
## iter 230 value 564.966814
## iter 240 value 562.556104
## iter 250 value 560.892197
## iter 260 value 558.994814
## iter 270 value 556.731284
## iter 280 value 555.208620
## iter 290 value 554.899883
## iter 300 value 554.884008
## iter 310 value 554.866694
## iter 320 value 554.797957
## iter 330 value 554.728978
## iter 340 value 554.710732
## iter 350 value 554.638751
## iter 360 value 554.445114
## iter 370 value 554.368936
## iter 380 value 554.273082
## iter 390 value 554.203010
## iter 400 value 554.140211
## iter 410 value 554.117987
## iter 420 value 554.065932
## iter 430 value 554.040054
## iter 440 value 554.038469
## iter 450 value 554.036801
## iter 460 value 554.034939
## iter 470 value 554.020212
## iter 480 value 554.004281
## iter 490 value 554.000690
## iter 500 value 553.991954
## final value 553.991954
## stopped after 500 iterations
## # weights: 106
## initial value 1403306.937650
## iter 10 value 1489.807939
## iter 20 value 1118.596824
## iter 30 value 916.295527
## iter 40 value 826.286299
## iter 50 value 756.741621
## iter 60 value 719.360531
## iter 70 value 673.942788
## iter 80 value 639.978483
## iter 90 value 600.701460
## iter 100 value 564.639069
## iter 110 value 540.355896
## iter 120 value 523.115761
## iter 130 value 501.586409
## iter 140 value 490.563376
## iter 150 value 480.253887
## iter 160 value 455.425952
## iter 170 value 435.455089
## iter 180 value 414.290597
## iter 190 value 390.477930
## iter 200 value 373.551827
## iter 210 value 361.995593
## iter 220 value 356.848363
## iter 230 value 354.638246
## iter 240 value 351.518882
## iter 250 value 346.375828
## iter 260 value 341.692049
## iter 270 value 336.120931
## iter 280 value 332.173052
## iter 290 value 327.864140
## iter 300 value 322.130651
## iter 310 value 317.172009
## iter 320 value 313.556966
## iter 330 value 302.769680
## iter 340 value 295.663151
## iter 350 value 290.818510
## iter 360 value 288.719066
## iter 370 value 286.828947
## iter 380 value 285.030791
## iter 390 value 283.897375
## iter 400 value 282.666274
## iter 410 value 282.038230
## iter 420 value 281.638377
## iter 430 value 281.094259
## iter 440 value 281.042650
## iter 450 value 280.966822
## iter 460 value 280.734646
## iter 470 value 280.122408
## iter 480 value 279.632591
## iter 490 value 279.255727
## iter 500 value 279.135127
## final value 279.135127
## stopped after 500 iterations
## # weights: 141
## initial value 1393306.512503
## iter 10 value 1617.631497
## iter 20 value 1051.249889
## iter 30 value 882.402373
## iter 40 value 761.195814
## iter 50 value 658.833216
## iter 60 value 587.677486
## iter 70 value 546.008241
## iter 80 value 497.987167
## iter 90 value 459.704980
## iter 100 value 429.703528
## iter 110 value 390.436897
## iter 120 value 362.546069
## iter 130 value 342.328978
## iter 140 value 321.231684
## iter 150 value 295.873518
## iter 160 value 272.439508
## iter 170 value 247.664324
## iter 180 value 237.300497
## iter 190 value 228.925650
## iter 200 value 223.642609
## iter 210 value 218.593996
## iter 220 value 213.977332
## iter 230 value 210.851311
## iter 240 value 206.941389
## iter 250 value 203.539263
## iter 260 value 201.824722
## iter 270 value 199.461087
## iter 280 value 197.808906
## iter 290 value 196.920781
## iter 300 value 196.555254
## iter 310 value 195.844090
## iter 320 value 194.231968
## iter 330 value 193.128309
## iter 340 value 191.420532
## iter 350 value 188.587819
## iter 360 value 184.520386
## iter 370 value 179.776055
## iter 380 value 175.859136
## iter 390 value 171.486382
## iter 400 value 166.632662
## iter 410 value 163.699258
## iter 420 value 161.636128
## iter 430 value 157.573503
## iter 440 value 153.444373
## iter 450 value 149.425640
## iter 460 value 146.048195
## iter 470 value 143.776179
## iter 480 value 142.624020
## iter 490 value 141.905486
## iter 500 value 141.439992
## final value 141.439992
## stopped after 500 iterations
## # weights: 15
## initial value 1412123.848788
## iter 10 value 17816.127669
## iter 20 value 4213.296761
## iter 30 value 3129.584344
## iter 40 value 2501.798233
## iter 50 value 2195.138494
## iter 60 value 1954.790678
## iter 70 value 1732.478504
## iter 80 value 1693.816072
## iter 90 value 1633.072117
## iter 100 value 1614.261929
## iter 110 value 1612.677947
## iter 120 value 1612.338431
## final value 1612.338009
## converged
## # weights: 36
## initial value 1431181.779951
## iter 10 value 9705.906100
## iter 20 value 7564.749401
## iter 30 value 6574.719690
## iter 40 value 3897.015955
## iter 50 value 2875.560804
## iter 60 value 2099.440875
## iter 70 value 1552.504797
## iter 80 value 1386.601634
## iter 90 value 1340.352030
## iter 100 value 1312.223614
## iter 110 value 1303.925527
## iter 120 value 1299.296974
## iter 130 value 1295.033185
## iter 140 value 1289.054234
## iter 150 value 1271.629045
## iter 160 value 1241.356303
## iter 170 value 1188.991495
## iter 180 value 1162.878653
## iter 190 value 1158.003381
## iter 200 value 1157.145021
## iter 210 value 1156.248857
## iter 220 value 1155.900267
## final value 1155.889083
## converged
## # weights: 71
## initial value 1403758.018815
## iter 10 value 2486.954813
## iter 20 value 1678.962782
## iter 30 value 1483.676972
## iter 40 value 1403.205838
## iter 50 value 1353.589959
## iter 60 value 1312.588254
## iter 70 value 1284.904676
## iter 80 value 1261.197500
## iter 90 value 1227.126801
## iter 100 value 1170.160491
## iter 110 value 1130.443836
## iter 120 value 1099.530558
## iter 130 value 1078.209711
## iter 140 value 1052.069029
## iter 150 value 1024.489084
## iter 160 value 1014.269621
## iter 170 value 997.177742
## iter 180 value 974.741669
## iter 190 value 951.376296
## iter 200 value 937.397438
## iter 210 value 927.014057
## iter 220 value 920.805077
## iter 230 value 911.812329
## iter 240 value 904.520730
## iter 250 value 901.543821
## iter 260 value 899.694674
## iter 270 value 898.236304
## iter 280 value 897.681775
## iter 290 value 897.239717
## iter 300 value 897.186828
## iter 310 value 897.098651
## iter 320 value 897.038988
## iter 330 value 896.982875
## iter 340 value 896.955409
## iter 350 value 896.944911
## final value 896.944785
## converged
## # weights: 106
## initial value 1382041.950975
## iter 10 value 1384.000628
## iter 20 value 1101.128149
## iter 30 value 987.671665
## iter 40 value 912.754629
## iter 50 value 858.698285
## iter 60 value 833.833243
## iter 70 value 799.096292
## iter 80 value 761.899846
## iter 90 value 736.607653
## iter 100 value 716.283311
## iter 110 value 701.648636
## iter 120 value 690.761524
## iter 130 value 676.008617
## iter 140 value 666.765224
## iter 150 value 661.799875
## iter 160 value 655.446013
## iter 170 value 650.198377
## iter 180 value 644.866209
## iter 190 value 638.226034
## iter 200 value 632.019667
## iter 210 value 628.095028
## iter 220 value 626.836556
## iter 230 value 625.387357
## iter 240 value 623.240263
## iter 250 value 621.699562
## iter 260 value 620.555860
## iter 270 value 619.360963
## iter 280 value 618.021215
## iter 290 value 617.169069
## iter 300 value 616.029885
## iter 310 value 614.909341
## iter 320 value 613.597784
## iter 330 value 611.404882
## iter 340 value 610.353821
## iter 350 value 609.832772
## iter 360 value 609.447249
## iter 370 value 609.308032
## iter 380 value 609.237639
## iter 390 value 609.180906
## iter 400 value 609.158370
## iter 410 value 609.139588
## iter 420 value 609.138905
## final value 609.138891
## converged
## # weights: 141
## initial value 1385092.616293
## iter 10 value 1948.394252
## iter 20 value 1232.669851
## iter 30 value 1102.571705
## iter 40 value 982.767266
## iter 50 value 875.009156
## iter 60 value 810.373744
## iter 70 value 777.303965
## iter 80 value 747.839620
## iter 90 value 726.065221
## iter 100 value 707.276226
## iter 110 value 695.120362
## iter 120 value 679.130091
## iter 130 value 659.575796
## iter 140 value 644.640796
## iter 150 value 637.986588
## iter 160 value 632.960017
## iter 170 value 628.043587
## iter 180 value 619.653308
## iter 190 value 615.223589
## iter 200 value 612.360386
## iter 210 value 610.238602
## iter 220 value 608.340191
## iter 230 value 605.428699
## iter 240 value 602.297260
## iter 250 value 599.588594
## iter 260 value 597.787583
## iter 270 value 595.135891
## iter 280 value 585.022136
## iter 290 value 575.466559
## iter 300 value 570.159954
## iter 310 value 562.084254
## iter 320 value 552.930719
## iter 330 value 545.582173
## iter 340 value 540.122187
## iter 350 value 535.265294
## iter 360 value 532.628995
## iter 370 value 530.071624
## iter 380 value 528.060579
## iter 390 value 526.262586
## iter 400 value 525.339962
## iter 410 value 524.892119
## iter 420 value 524.023242
## iter 430 value 522.718460
## iter 440 value 522.069341
## iter 450 value 521.529123
## iter 460 value 521.346000
## iter 470 value 521.324263
## iter 480 value 521.314622
## iter 490 value 521.309971
## iter 500 value 521.307539
## final value 521.307539
## stopped after 500 iterations
## # weights: 15
## initial value 1432508.485107
## iter 10 value 5339.346468
## iter 20 value 3559.675302
## iter 30 value 2226.302987
## iter 40 value 1816.054506
## iter 50 value 1775.609031
## iter 60 value 1743.468383
## iter 70 value 1735.108816
## iter 80 value 1730.896427
## iter 90 value 1641.715324
## iter 100 value 1501.577674
## iter 110 value 1427.786525
## iter 120 value 1373.888751
## iter 130 value 1267.663270
## iter 140 value 1187.856222
## iter 150 value 1180.076137
## iter 160 value 1172.141917
## iter 170 value 1167.677887
## iter 180 value 1167.273107
## iter 190 value 1165.696008
## iter 200 value 1164.762732
## iter 210 value 1164.669219
## iter 220 value 1164.615670
## iter 230 value 1164.592541
## final value 1164.591750
## converged
## # weights: 36
## initial value 1388708.885180
## iter 10 value 5973.882990
## iter 20 value 4403.024030
## iter 30 value 3904.147124
## iter 40 value 3212.204664
## iter 50 value 2719.799176
## iter 60 value 2223.515068
## iter 70 value 2096.897718
## iter 80 value 1989.025444
## iter 90 value 1904.602705
## iter 100 value 1474.363221
## iter 110 value 1399.616047
## iter 120 value 1350.869603
## iter 130 value 1317.414053
## iter 140 value 1279.222464
## iter 150 value 1260.784377
## iter 160 value 1245.351813
## iter 170 value 1182.713357
## iter 180 value 1159.840787
## iter 190 value 1122.465503
## iter 200 value 1097.101789
## iter 210 value 1088.784953
## iter 220 value 1080.037009
## iter 230 value 1074.656202
## iter 240 value 1051.272103
## iter 250 value 1011.099836
## iter 260 value 998.719834
## iter 270 value 992.754856
## iter 280 value 970.061953
## iter 290 value 963.994104
## iter 300 value 958.076888
## iter 310 value 957.154108
## iter 320 value 956.992009
## final value 956.960470
## converged
## # weights: 71
## initial value 1414060.565511
## iter 10 value 1470.441608
## iter 20 value 1140.396857
## iter 30 value 1005.112034
## iter 40 value 944.503247
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## iter 370 value 546.703356
## iter 380 value 546.650465
## iter 390 value 546.637810
## iter 400 value 546.636447
## final value 546.636159
## converged
## # weights: 106
## initial value 1417058.631998
## iter 10 value 1460.257960
## iter 20 value 1093.108040
## iter 30 value 968.028515
## iter 40 value 888.312314
## iter 50 value 810.182400
## iter 60 value 724.166239
## iter 70 value 660.608056
## iter 80 value 614.044278
## iter 90 value 585.511991
## iter 100 value 550.889973
## iter 110 value 513.191518
## iter 120 value 485.544687
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## iter 250 value 415.450995
## iter 260 value 412.227896
## iter 270 value 408.006856
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## iter 300 value 401.025740
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## iter 380 value 398.793240
## iter 390 value 398.788796
## iter 400 value 398.786717
## iter 410 value 398.786553
## final value 398.786536
## converged
## # weights: 141
## initial value 1355818.693137
## iter 10 value 1360.133812
## iter 20 value 984.913011
## iter 30 value 845.216891
## iter 40 value 768.562998
## iter 50 value 719.519982
## iter 60 value 672.602200
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## iter 80 value 597.843307
## iter 90 value 558.094847
## iter 100 value 526.895491
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## iter 150 value 394.428138
## iter 160 value 376.466515
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## iter 180 value 341.864542
## iter 190 value 325.501222
## iter 200 value 313.291401
## iter 210 value 299.809674
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## iter 230 value 284.561389
## iter 240 value 278.425721
## iter 250 value 273.471175
## iter 260 value 269.552468
## iter 270 value 266.868983
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## iter 300 value 262.827835
## iter 310 value 262.043976
## iter 320 value 260.800474
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## iter 340 value 256.411733
## iter 350 value 254.606692
## iter 360 value 252.353112
## iter 370 value 247.924148
## iter 380 value 243.943318
## iter 390 value 241.384897
## iter 400 value 238.193853
## iter 410 value 235.313416
## iter 420 value 233.479646
## iter 430 value 232.330667
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## iter 460 value 225.454505
## iter 470 value 223.863862
## iter 480 value 222.110833
## iter 490 value 220.282719
## iter 500 value 218.407044
## final value 218.407044
## stopped after 500 iterations
## # weights: 15
## initial value 1389878.342298
## iter 10 value 11181.065855
## iter 20 value 4652.209575
## iter 30 value 1886.737413
## iter 40 value 1832.986654
## iter 50 value 1687.531635
## iter 60 value 1634.128802
## iter 70 value 1569.450801
## iter 80 value 1548.248073
## iter 90 value 1514.846917
## iter 100 value 1504.965066
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## iter 160 value 1386.996789
## iter 170 value 1349.824249
## iter 180 value 1263.141703
## iter 190 value 1211.903138
## iter 200 value 1191.422154
## iter 210 value 1160.507839
## final value 1159.227722
## converged
## # weights: 36
## initial value 1379452.138405
## iter 10 value 470956.100085
## iter 20 value 6956.222637
## iter 30 value 5359.511024
## iter 40 value 4540.502955
## iter 50 value 3698.589962
## iter 60 value 2777.832924
## iter 70 value 2162.238570
## iter 80 value 1734.382695
## iter 90 value 1724.536820
## iter 100 value 1722.141507
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## iter 120 value 1698.836358
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## iter 210 value 1566.904285
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## iter 240 value 1540.398685
## iter 250 value 1488.396689
## iter 260 value 1449.493516
## iter 270 value 1444.205081
## iter 280 value 1442.545355
## iter 290 value 1441.052094
## iter 300 value 1439.595921
## iter 310 value 1438.086840
## iter 320 value 1438.058631
## iter 330 value 1437.845194
## iter 340 value 1436.132884
## iter 350 value 1418.804879
## iter 360 value 1414.719343
## iter 370 value 1409.272767
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## iter 470 value 1166.562136
## iter 480 value 1123.299296
## iter 490 value 1090.519466
## iter 500 value 1075.379096
## final value 1075.379096
## stopped after 500 iterations
## # weights: 71
## initial value 1404574.992625
## iter 10 value 2497.312276
## iter 20 value 1689.574488
## iter 30 value 1259.310928
## iter 40 value 1128.798406
## iter 50 value 1063.422824
## iter 60 value 1011.441808
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## iter 80 value 946.850245
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## iter 470 value 656.063977
## iter 480 value 642.541912
## iter 490 value 640.109619
## iter 500 value 637.287794
## final value 637.287794
## stopped after 500 iterations
## # weights: 106
## initial value 1466744.576828
## iter 10 value 1949.525647
## iter 20 value 1198.145707
## iter 30 value 961.891397
## iter 40 value 847.673786
## iter 50 value 796.717697
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## iter 70 value 710.039563
## iter 80 value 677.077790
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## iter 300 value 320.467133
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## iter 480 value 272.308980
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## iter 500 value 270.698108
## final value 270.698108
## stopped after 500 iterations
## # weights: 141
## initial value 1374891.404402
## iter 10 value 1386.133672
## iter 20 value 1071.423448
## iter 30 value 886.405820
## iter 40 value 788.568656
## iter 50 value 713.049041
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## iter 80 value 560.402695
## iter 90 value 529.742159
## iter 100 value 488.734416
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## iter 390 value 213.965555
## iter 400 value 212.547285
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## iter 470 value 202.927265
## iter 480 value 202.249639
## iter 490 value 202.034761
## iter 500 value 201.815602
## final value 201.815602
## stopped after 500 iterations
## # weights: 15
## initial value 1420774.165213
## iter 10 value 6414.129254
## iter 20 value 5813.352589
## iter 30 value 4862.960337
## iter 40 value 3268.292716
## iter 50 value 2462.536656
## iter 60 value 1611.251157
## iter 70 value 1370.345345
## iter 80 value 1342.294010
## iter 90 value 1285.429562
## iter 100 value 1224.400646
## iter 110 value 1209.957789
## iter 120 value 1175.414182
## iter 130 value 1162.775154
## iter 140 value 1162.122544
## iter 150 value 1161.279417
## iter 160 value 1158.898149
## iter 170 value 1158.601927
## iter 180 value 1158.543898
## iter 190 value 1157.753860
## iter 200 value 1157.544140
## iter 210 value 1157.537344
## iter 220 value 1157.391520
## iter 230 value 1157.294973
## final value 1157.294902
## converged
## # weights: 36
## initial value 1417826.813888
## iter 10 value 5918.596206
## iter 20 value 3038.662120
## iter 30 value 1932.067251
## iter 40 value 1392.560625
## iter 50 value 1254.338043
## iter 60 value 1198.402287
## iter 70 value 1177.932116
## iter 80 value 1171.554781
## iter 90 value 1161.518735
## iter 100 value 1150.224216
## iter 110 value 1119.589898
## iter 120 value 1092.283455
## iter 130 value 1080.965825
## iter 140 value 1080.146157
## iter 150 value 1079.263465
## iter 160 value 1078.992786
## iter 170 value 1078.980893
## iter 180 value 1078.896782
## iter 180 value 1078.896771
## final value 1078.896771
## converged
## # weights: 71
## initial value 1415081.982814
## iter 10 value 3553.481708
## iter 20 value 2218.502935
## iter 30 value 1404.725219
## iter 40 value 1145.963674
## iter 50 value 995.315998
## iter 60 value 945.009644
## iter 70 value 914.582198
## iter 80 value 896.400935
## iter 90 value 861.382400
## iter 100 value 821.374482
## iter 110 value 802.074961
## iter 120 value 778.199624
## iter 130 value 751.993940
## iter 140 value 719.141515
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## iter 160 value 687.569444
## iter 170 value 681.053937
## iter 180 value 663.516120
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## iter 200 value 633.201311
## iter 210 value 624.321667
## iter 220 value 623.033257
## iter 230 value 620.209512
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## iter 250 value 602.972520
## iter 260 value 591.362749
## iter 270 value 576.650362
## iter 280 value 570.809524
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## iter 300 value 564.307827
## iter 310 value 563.653259
## iter 320 value 563.306345
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## iter 340 value 561.735426
## iter 350 value 560.613991
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## iter 380 value 559.716511
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## iter 400 value 557.435441
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## iter 460 value 549.626636
## iter 470 value 549.417577
## iter 480 value 548.160726
## iter 490 value 546.687296
## iter 500 value 544.842008
## final value 544.842008
## stopped after 500 iterations
## # weights: 106
## initial value 1439929.964485
## iter 10 value 1552.564060
## iter 20 value 1034.583771
## iter 30 value 895.904341
## iter 40 value 823.484717
## iter 50 value 785.700622
## iter 60 value 742.081468
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## iter 290 value 388.247129
## iter 300 value 377.726577
## iter 310 value 365.691521
## iter 320 value 359.743445
## iter 330 value 351.115545
## iter 340 value 348.127766
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## iter 400 value 339.782062
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## iter 470 value 335.416426
## iter 480 value 335.180318
## iter 490 value 335.004578
## iter 500 value 334.906565
## final value 334.906565
## stopped after 500 iterations
## # weights: 141
## initial value 1429753.188883
## iter 10 value 1845.074529
## iter 20 value 1256.776382
## iter 30 value 974.823715
## iter 40 value 794.899919
## iter 50 value 676.207072
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## iter 90 value 469.203647
## iter 100 value 446.107793
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## iter 120 value 399.815076
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## iter 140 value 350.493586
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## iter 330 value 233.222352
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## iter 470 value 224.493583
## iter 480 value 222.455905
## iter 490 value 221.718653
## iter 500 value 221.511772
## final value 221.511772
## stopped after 500 iterations
## # weights: 15
## initial value 1425529.190665
## iter 10 value 4416.080235
## iter 20 value 2580.906070
## iter 30 value 2013.762357
## iter 40 value 1756.856814
## iter 50 value 1567.975340
## iter 60 value 1524.131971
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## iter 80 value 1470.899508
## iter 90 value 1452.443466
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## iter 250 value 1399.515880
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## iter 270 value 1397.063469
## iter 280 value 1396.246454
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## iter 320 value 1393.613246
## iter 330 value 1391.605102
## iter 340 value 1390.320633
## iter 350 value 1390.280897
## iter 360 value 1388.864965
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## iter 400 value 1384.122573
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## iter 470 value 1379.374598
## iter 480 value 1379.272724
## iter 490 value 1377.977934
## iter 500 value 1377.628995
## final value 1377.628995
## stopped after 500 iterations
## # weights: 36
## initial value 1372062.450316
## iter 10 value 3559.658349
## iter 20 value 2090.046609
## iter 30 value 1731.799937
## iter 40 value 1344.075155
## iter 50 value 1042.843572
## iter 60 value 974.158913
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## iter 80 value 927.231012
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## iter 500 value 771.678847
## final value 771.678847
## stopped after 500 iterations
## # weights: 71
## initial value 1407405.516296
## iter 10 value 1285.530881
## iter 20 value 1081.169193
## iter 30 value 1005.220023
## iter 40 value 968.939347
## iter 50 value 913.203922
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## iter 460 value 424.104702
## iter 470 value 424.087131
## iter 480 value 424.081078
## iter 490 value 424.071059
## iter 500 value 424.058395
## final value 424.058395
## stopped after 500 iterations
## # weights: 106
## initial value 1424814.260133
## iter 10 value 1594.538846
## iter 20 value 1102.098337
## iter 30 value 991.671078
## iter 40 value 893.912976
## iter 50 value 862.575916
## iter 60 value 809.624436
## iter 70 value 729.349119
## iter 80 value 671.163613
## iter 90 value 626.477590
## iter 100 value 594.128246
## iter 110 value 561.441133
## iter 120 value 533.954674
## iter 130 value 518.786171
## iter 140 value 507.558350
## iter 150 value 497.010362
## iter 160 value 487.370351
## iter 170 value 475.418923
## iter 180 value 462.441269
## iter 190 value 450.688995
## iter 200 value 434.668248
## iter 210 value 419.718570
## iter 220 value 414.562902
## iter 230 value 409.408949
## iter 240 value 399.930681
## iter 250 value 390.923710
## iter 260 value 372.793727
## iter 270 value 357.867706
## iter 280 value 349.582949
## iter 290 value 342.621101
## iter 300 value 335.503580
## iter 310 value 329.318959
## iter 320 value 322.076818
## iter 330 value 316.336668
## iter 340 value 312.642786
## iter 350 value 310.383316
## iter 360 value 308.764523
## iter 370 value 307.190392
## iter 380 value 306.120662
## iter 390 value 304.759661
## iter 400 value 303.247248
## iter 410 value 301.866282
## iter 420 value 301.447400
## iter 430 value 301.216995
## iter 440 value 301.173436
## iter 450 value 301.102476
## iter 460 value 301.025853
## iter 470 value 300.919934
## iter 480 value 300.843856
## iter 490 value 300.785988
## iter 500 value 300.743492
## final value 300.743492
## stopped after 500 iterations
## # weights: 141
## initial value 1374718.962443
## iter 10 value 3867.947258
## iter 20 value 1181.418014
## iter 30 value 917.745772
## iter 40 value 730.277187
## iter 50 value 629.976521
## iter 60 value 560.904120
## iter 70 value 508.384994
## iter 80 value 479.468314
## iter 90 value 455.683099
## iter 100 value 433.409400
## iter 110 value 406.792641
## iter 120 value 389.212414
## iter 130 value 370.770323
## iter 140 value 360.463879
## iter 150 value 352.582510
## iter 160 value 347.151858
## iter 170 value 342.584044
## iter 180 value 338.536125
## iter 190 value 333.363048
## iter 200 value 331.222135
## iter 210 value 328.358136
## iter 220 value 325.539217
## iter 230 value 321.973662
## iter 240 value 316.170390
## iter 250 value 309.502115
## iter 260 value 301.747857
## iter 270 value 297.624084
## iter 280 value 294.009404
## iter 290 value 292.743939
## iter 300 value 292.161368
## iter 310 value 291.300523
## iter 320 value 289.169547
## iter 330 value 287.357796
## iter 340 value 285.450748
## iter 350 value 282.538819
## iter 360 value 279.754132
## iter 370 value 274.362949
## iter 380 value 270.195928
## iter 390 value 265.590122
## iter 400 value 261.111312
## iter 410 value 255.563835
## iter 420 value 250.297615
## iter 430 value 246.809215
## iter 440 value 244.839308
## iter 450 value 243.157614
## iter 460 value 241.861797
## iter 470 value 240.940082
## iter 480 value 240.353628
## iter 490 value 239.881087
## iter 500 value 239.169566
## final value 239.169566
## stopped after 500 iterations
## # weights: 15
## initial value 1431873.437992
## iter 10 value 11878.146060
## iter 20 value 6578.310020
## iter 30 value 5704.810366
## iter 40 value 3808.295340
## iter 50 value 2442.939464
## iter 60 value 1776.195343
## iter 70 value 1579.653803
## iter 80 value 1495.946338
## iter 90 value 1473.417703
## iter 100 value 1416.139657
## iter 110 value 1396.739612
## iter 120 value 1394.396687
## iter 130 value 1394.189830
## final value 1394.189776
## converged
## # weights: 36
## initial value 1430331.198013
## iter 10 value 18676.235133
## iter 20 value 9513.110260
## iter 30 value 6750.324980
## iter 40 value 4806.755940
## iter 50 value 3614.906157
## iter 60 value 2645.032578
## iter 70 value 2233.237310
## iter 80 value 1948.274621
## iter 90 value 1765.058969
## iter 100 value 1620.401578
## iter 110 value 1431.533548
## iter 120 value 1344.381896
## iter 130 value 1295.481241
## iter 140 value 1260.349687
## iter 150 value 1234.615332
## iter 160 value 1215.679553
## iter 170 value 1198.309315
## iter 180 value 1155.842502
## iter 190 value 1134.255219
## iter 200 value 1125.618284
## iter 210 value 1124.785430
## iter 220 value 1124.672863
## iter 230 value 1124.632976
## iter 240 value 1124.590404
## iter 250 value 1124.495576
## iter 260 value 1124.429942
## final value 1124.429772
## converged
## # weights: 71
## initial value 1368000.667055
## iter 10 value 1850.053102
## iter 20 value 1265.692526
## iter 30 value 1171.863894
## iter 40 value 1118.397806
## iter 50 value 1080.917850
## iter 60 value 1036.436256
## iter 70 value 1009.061296
## iter 80 value 987.113551
## iter 90 value 971.971624
## iter 100 value 959.849128
## iter 110 value 949.056143
## iter 120 value 935.716173
## iter 130 value 926.788659
## iter 140 value 919.760793
## iter 150 value 915.049127
## iter 160 value 910.306834
## iter 170 value 903.357062
## iter 180 value 896.366872
## iter 190 value 893.761901
## iter 200 value 892.293931
## iter 210 value 890.109988
## iter 220 value 884.053138
## iter 230 value 880.127699
## iter 240 value 879.424549
## iter 250 value 879.231307
## iter 260 value 879.217898
## final value 879.217409
## converged
## # weights: 106
## initial value 1351450.994197
## iter 10 value 1383.054146
## iter 20 value 1147.076492
## iter 30 value 1023.484655
## iter 40 value 929.947252
## iter 50 value 877.216886
## iter 60 value 848.801645
## iter 70 value 810.846709
## iter 80 value 781.267550
## iter 90 value 763.936016
## iter 100 value 754.845719
## iter 110 value 732.981916
## iter 120 value 695.279141
## iter 130 value 674.587016
## iter 140 value 664.015421
## iter 150 value 658.033951
## iter 160 value 652.472670
## iter 170 value 646.477523
## iter 180 value 643.892102
## iter 190 value 638.846179
## iter 200 value 633.025087
## iter 210 value 631.224742
## iter 220 value 629.418101
## iter 230 value 627.069737
## iter 240 value 623.763129
## iter 250 value 621.789205
## iter 260 value 620.498994
## iter 270 value 619.723572
## iter 280 value 619.323303
## iter 290 value 619.022169
## iter 300 value 618.753879
## iter 310 value 618.635870
## iter 320 value 618.611610
## final value 618.605267
## converged
## # weights: 141
## initial value 1414573.195862
## iter 10 value 1738.543625
## iter 20 value 1175.618192
## iter 30 value 1038.700648
## iter 40 value 987.785092
## iter 50 value 913.653123
## iter 60 value 855.534047
## iter 70 value 810.916695
## iter 80 value 779.537571
## iter 90 value 748.333623
## iter 100 value 720.801955
## iter 110 value 697.199757
## iter 120 value 680.627682
## iter 130 value 665.197338
## iter 140 value 648.901316
## iter 150 value 631.947465
## iter 160 value 623.766745
## iter 170 value 617.762456
## iter 180 value 610.083020
## iter 190 value 598.965711
## iter 200 value 589.640915
## iter 210 value 584.270266
## iter 220 value 581.519352
## iter 230 value 579.075103
## iter 240 value 577.163564
## iter 250 value 576.039855
## iter 260 value 574.916167
## iter 270 value 572.315351
## iter 280 value 569.811148
## iter 290 value 568.816832
## iter 300 value 567.639118
## iter 310 value 565.983362
## iter 320 value 563.903763
## iter 330 value 561.236246
## iter 340 value 557.659080
## iter 350 value 553.892787
## iter 360 value 549.182778
## iter 370 value 540.612877
## iter 380 value 534.731396
## iter 390 value 530.054984
## iter 400 value 525.767070
## iter 410 value 523.294809
## iter 420 value 519.093696
## iter 430 value 516.317632
## iter 440 value 515.010576
## iter 450 value 514.337925
## iter 460 value 513.968352
## iter 470 value 513.805914
## iter 480 value 513.767460
## iter 490 value 513.752568
## iter 500 value 513.748522
## final value 513.748522
## stopped after 500 iterations
## # weights: 15
## initial value 1389376.583573
## iter 10 value 42513.885861
## iter 20 value 7760.089986
## iter 30 value 4647.309785
## iter 40 value 2821.511176
## iter 50 value 1697.598772
## iter 60 value 1416.596018
## iter 70 value 1350.818268
## iter 80 value 1264.669605
## iter 90 value 1194.266874
## iter 100 value 1165.181000
## iter 110 value 1143.335251
## iter 120 value 1128.393794
## iter 130 value 1124.758777
## iter 140 value 1123.639926
## iter 150 value 1121.896772
## iter 160 value 1121.493406
## iter 170 value 1121.321700
## iter 180 value 1121.142484
## final value 1121.130155
## converged
## # weights: 36
## initial value 1426640.918311
## iter 10 value 305748.306450
## iter 20 value 201935.940094
## iter 30 value 7851.023625
## iter 40 value 5881.046265
## iter 50 value 5626.968655
## iter 60 value 5443.310648
## iter 70 value 3891.670765
## iter 80 value 3058.910714
## iter 90 value 2519.763894
## iter 100 value 2010.773381
## iter 110 value 1834.282124
## iter 120 value 1707.739772
## iter 130 value 1439.368209
## iter 140 value 1398.847167
## iter 150 value 1316.421473
## iter 160 value 1297.961909
## iter 170 value 1205.379355
## iter 180 value 1144.416753
## iter 190 value 1103.867625
## iter 200 value 1080.196190
## iter 210 value 1045.569477
## iter 220 value 1030.168361
## iter 230 value 1027.902224
## iter 240 value 1024.874845
## iter 250 value 1018.938487
## iter 260 value 1009.808285
## iter 270 value 997.216460
## iter 280 value 966.500811
## iter 290 value 938.936122
## iter 300 value 930.124601
## iter 310 value 924.429835
## iter 320 value 918.416532
## iter 330 value 912.237135
## iter 340 value 907.474656
## iter 350 value 906.090403
## iter 360 value 904.053334
## iter 370 value 903.172606
## iter 380 value 903.021779
## iter 390 value 902.756015
## iter 400 value 902.220670
## iter 410 value 902.100105
## iter 420 value 902.080627
## iter 430 value 902.075992
## final value 902.075688
## converged
## # weights: 71
## initial value 1394268.565642
## iter 10 value 1873.712711
## iter 20 value 1317.003912
## iter 30 value 1094.609181
## iter 40 value 999.750983
## iter 50 value 937.663120
## iter 60 value 906.037934
## iter 70 value 872.826445
## iter 80 value 844.494706
## iter 90 value 803.824136
## iter 100 value 748.359752
## iter 110 value 738.653201
## iter 120 value 721.366946
## iter 130 value 705.023144
## iter 140 value 698.711951
## iter 150 value 697.537037
## iter 160 value 697.010131
## iter 170 value 694.681049
## iter 180 value 688.978444
## iter 190 value 682.577655
## iter 200 value 678.197417
## iter 210 value 670.835703
## iter 220 value 664.482069
## iter 230 value 660.525414
## iter 240 value 657.694948
## iter 250 value 654.533808
## iter 260 value 652.340445
## iter 270 value 650.125552
## iter 280 value 649.415338
## iter 290 value 649.246118
## iter 300 value 649.204906
## iter 310 value 649.168164
## iter 320 value 649.116135
## iter 330 value 649.026350
## iter 340 value 648.852544
## iter 350 value 648.744976
## iter 360 value 648.708102
## iter 370 value 648.675190
## iter 380 value 648.670736
## iter 390 value 648.669079
## final value 648.668996
## converged
## # weights: 106
## initial value 1417638.523158
## iter 10 value 1795.865423
## iter 20 value 1006.698836
## iter 30 value 879.507381
## iter 40 value 799.718596
## iter 50 value 747.307981
## iter 60 value 716.944440
## iter 70 value 651.785899
## iter 80 value 591.141017
## iter 90 value 559.055881
## iter 100 value 521.956807
## iter 110 value 485.087138
## iter 120 value 459.083686
## iter 130 value 427.732516
## iter 140 value 411.810075
## iter 150 value 394.458348
## iter 160 value 384.757527
## iter 170 value 372.763265
## iter 180 value 362.352760
## iter 190 value 346.996447
## iter 200 value 340.624309
## iter 210 value 335.872534
## iter 220 value 333.068989
## iter 230 value 330.730462
## iter 240 value 327.965220
## iter 250 value 323.747396
## iter 260 value 317.945625
## iter 270 value 314.161351
## iter 280 value 310.872620
## iter 290 value 306.041003
## iter 300 value 300.063982
## iter 310 value 297.073617
## iter 320 value 295.519161
## iter 330 value 294.576755
## iter 340 value 294.161286
## iter 350 value 294.007460
## iter 360 value 293.947444
## iter 370 value 293.680345
## iter 380 value 292.722926
## iter 390 value 291.119413
## iter 400 value 290.113679
## iter 410 value 289.913108
## iter 420 value 289.873115
## iter 430 value 289.867138
## iter 440 value 289.866708
## iter 450 value 289.865698
## iter 460 value 289.863793
## iter 470 value 289.860844
## iter 480 value 289.858005
## iter 490 value 289.855406
## iter 500 value 289.853558
## final value 289.853558
## stopped after 500 iterations
## # weights: 141
## initial value 1397917.031843
## iter 10 value 1359.837703
## iter 20 value 1056.886160
## iter 30 value 888.798096
## iter 40 value 816.433030
## iter 50 value 720.584238
## iter 60 value 617.674906
## iter 70 value 524.957506
## iter 80 value 486.309386
## iter 90 value 444.959992
## iter 100 value 421.098888
## iter 110 value 386.121525
## iter 120 value 351.675891
## iter 130 value 336.895899
## iter 140 value 323.907115
## iter 150 value 308.911057
## iter 160 value 290.344204
## iter 170 value 277.100566
## iter 180 value 268.845961
## iter 190 value 261.411070
## iter 200 value 254.461326
## iter 210 value 245.373395
## iter 220 value 237.259665
## iter 230 value 231.892659
## iter 240 value 228.379697
## iter 250 value 225.165446
## iter 260 value 222.501844
## iter 270 value 219.469101
## iter 280 value 217.870288
## iter 290 value 217.446960
## iter 300 value 217.110444
## iter 310 value 216.729673
## iter 320 value 216.115111
## iter 330 value 215.349188
## iter 340 value 214.093226
## iter 350 value 212.505758
## iter 360 value 211.019023
## iter 370 value 209.027055
## iter 380 value 207.599435
## iter 390 value 205.898556
## iter 400 value 204.266096
## iter 410 value 202.879831
## iter 420 value 201.225020
## iter 430 value 199.766989
## iter 440 value 197.682816
## iter 450 value 196.172051
## iter 460 value 194.850052
## iter 470 value 193.808291
## iter 480 value 193.203684
## iter 490 value 192.699709
## iter 500 value 192.429809
## final value 192.429809
## stopped after 500 iterations
## # weights: 15
## initial value 1418647.988848
## iter 10 value 7802.726941
## iter 20 value 3190.461116
## iter 30 value 1701.366337
## iter 40 value 1555.977145
## iter 50 value 1471.532427
## iter 60 value 1404.936526
## iter 70 value 1201.489434
## iter 80 value 1140.062653
## iter 90 value 1134.717964
## iter 100 value 1124.395355
## iter 110 value 1121.952263
## iter 120 value 1121.358112
## iter 130 value 1119.606285
## iter 140 value 1118.254126
## iter 150 value 1118.197345
## iter 160 value 1117.807172
## iter 170 value 1116.931237
## iter 180 value 1116.921685
## iter 190 value 1116.867487
## iter 200 value 1116.533878
## iter 210 value 1116.510917
## iter 220 value 1116.493598
## iter 230 value 1116.446357
## final value 1116.445325
## converged
## # weights: 36
## initial value 1427170.219825
## iter 10 value 39071.045249
## iter 20 value 9279.542911
## iter 30 value 2874.803980
## iter 40 value 1736.277200
## iter 50 value 1497.026126
## iter 60 value 1373.409921
## iter 70 value 1339.471009
## iter 80 value 1313.762026
## iter 90 value 1261.645624
## iter 100 value 1250.490423
## iter 110 value 1245.566963
## iter 120 value 1241.579924
## iter 130 value 1239.196521
## iter 140 value 1217.123366
## iter 150 value 1174.713316
## iter 160 value 1167.636967
## iter 170 value 1161.038392
## iter 180 value 1156.729875
## iter 190 value 1155.155791
## iter 200 value 1149.459696
## iter 210 value 1148.276345
## iter 220 value 1145.858745
## iter 230 value 1145.453377
## iter 240 value 1144.060618
## iter 250 value 1143.535749
## iter 260 value 1143.404843
## iter 270 value 1141.279029
## iter 280 value 1121.017612
## iter 290 value 1087.147054
## iter 300 value 1066.275535
## iter 310 value 1055.023777
## iter 320 value 1049.716266
## iter 330 value 1046.276794
## iter 340 value 1042.206722
## iter 350 value 1031.494478
## iter 360 value 1025.534778
## iter 370 value 1021.870643
## iter 380 value 1019.831706
## iter 390 value 1018.671908
## iter 400 value 1018.144157
## iter 410 value 1017.944022
## iter 420 value 1017.693434
## iter 430 value 1017.633236
## iter 440 value 1017.506815
## iter 450 value 1017.309112
## iter 460 value 1017.286308
## iter 470 value 1017.268432
## iter 470 value 1017.268426
## final value 1017.268426
## converged
## # weights: 71
## initial value 1426719.876281
## iter 10 value 1783.190871
## iter 20 value 1114.935635
## iter 30 value 992.551670
## iter 40 value 917.560367
## iter 50 value 857.733813
## iter 60 value 821.912889
## iter 70 value 800.687860
## iter 80 value 789.036687
## iter 90 value 782.639580
## iter 100 value 770.919928
## iter 110 value 764.131194
## iter 120 value 758.322088
## iter 130 value 755.538800
## iter 140 value 754.511775
## iter 150 value 753.983869
## iter 160 value 753.516688
## iter 170 value 752.056473
## iter 180 value 748.354181
## iter 190 value 746.074462
## iter 200 value 740.436190
## iter 210 value 729.813844
## iter 220 value 723.273467
## iter 230 value 719.326572
## iter 240 value 710.953646
## iter 250 value 704.119830
## iter 260 value 698.136399
## iter 270 value 695.641047
## iter 280 value 692.206845
## iter 290 value 689.423838
## iter 300 value 688.609514
## iter 310 value 687.874940
## iter 320 value 686.510198
## iter 330 value 686.074217
## iter 340 value 685.927384
## iter 350 value 685.658303
## iter 360 value 685.486237
## iter 370 value 684.585305
## iter 380 value 682.903028
## iter 390 value 682.160421
## iter 400 value 681.188697
## iter 410 value 680.507383
## iter 420 value 680.214694
## iter 430 value 680.185311
## iter 440 value 680.061735
## iter 450 value 680.041336
## iter 460 value 680.034540
## iter 460 value 680.034534
## iter 460 value 680.034532
## final value 680.034532
## converged
## # weights: 106
## initial value 1381374.794335
## iter 10 value 1299.734265
## iter 20 value 1056.887523
## iter 30 value 924.246473
## iter 40 value 820.543008
## iter 50 value 771.663785
## iter 60 value 708.455744
## iter 70 value 663.519686
## iter 80 value 636.717381
## iter 90 value 608.681548
## iter 100 value 597.299079
## iter 110 value 574.245431
## iter 120 value 537.280028
## iter 130 value 512.425534
## iter 140 value 490.554618
## iter 150 value 462.802398
## iter 160 value 442.416228
## iter 170 value 422.548174
## iter 180 value 411.453751
## iter 190 value 400.732569
## iter 200 value 393.325163
## iter 210 value 388.684376
## iter 220 value 387.294850
## iter 230 value 385.792397
## iter 240 value 383.828614
## iter 250 value 379.613521
## iter 260 value 375.572724
## iter 270 value 367.784379
## iter 280 value 358.778228
## iter 290 value 352.815450
## iter 300 value 348.352702
## iter 310 value 341.713739
## iter 320 value 337.311272
## iter 330 value 333.400939
## iter 340 value 329.210252
## iter 350 value 326.817503
## iter 360 value 324.349044
## iter 370 value 322.528903
## iter 380 value 312.804611
## iter 390 value 309.146648
## iter 400 value 305.206635
## iter 410 value 303.031465
## iter 420 value 301.705990
## iter 430 value 301.186973
## iter 440 value 301.102088
## iter 450 value 300.964212
## iter 460 value 300.765090
## iter 470 value 300.643788
## iter 480 value 300.526312
## iter 490 value 300.414483
## iter 500 value 300.309006
## final value 300.309006
## stopped after 500 iterations
## # weights: 141
## initial value 1348649.262086
## iter 10 value 1797.641927
## iter 20 value 1103.834976
## iter 30 value 959.107228
## iter 40 value 863.552441
## iter 50 value 777.611731
## iter 60 value 688.022225
## iter 70 value 600.893262
## iter 80 value 546.384911
## iter 90 value 493.258586
## iter 100 value 445.222399
## iter 110 value 416.494274
## iter 120 value 397.464881
## iter 130 value 385.392480
## iter 140 value 371.794476
## iter 150 value 355.871300
## iter 160 value 334.104825
## iter 170 value 311.003225
## iter 180 value 298.266561
## iter 190 value 288.192667
## iter 200 value 279.541700
## iter 210 value 270.401998
## iter 220 value 262.201877
## iter 230 value 251.713556
## iter 240 value 246.065889
## iter 250 value 241.961546
## iter 260 value 239.441350
## iter 270 value 237.927797
## iter 280 value 236.833032
## iter 290 value 236.402665
## iter 300 value 235.956323
## iter 310 value 234.896134
## iter 320 value 233.127371
## iter 330 value 231.783961
## iter 340 value 229.847097
## iter 350 value 226.199324
## iter 360 value 223.951083
## iter 370 value 221.235639
## iter 380 value 218.266239
## iter 390 value 215.617201
## iter 400 value 214.288562
## iter 410 value 212.057061
## iter 420 value 210.885106
## iter 430 value 208.335861
## iter 440 value 206.301861
## iter 450 value 205.231426
## iter 460 value 204.470067
## iter 470 value 203.790337
## iter 480 value 203.617950
## iter 490 value 203.526646
## iter 500 value 203.453954
## final value 203.453954
## stopped after 500 iterations
## # weights: 15
## initial value 1437213.302387
## iter 10 value 5255.207331
## iter 20 value 3949.388728
## iter 30 value 2290.690213
## iter 40 value 1836.722757
## iter 50 value 1770.770430
## iter 60 value 1751.021590
## iter 70 value 1739.954030
## iter 80 value 1736.133683
## iter 90 value 1735.518402
## iter 90 value 1735.518397
## final value 1735.517841
## converged
## # weights: 36
## initial value 1403685.143634
## iter 10 value 7291.596634
## iter 20 value 3345.667307
## iter 30 value 2854.230526
## iter 40 value 2735.652249
## iter 50 value 2662.486507
## iter 60 value 2568.819529
## iter 70 value 2219.920956
## iter 80 value 1980.937463
## iter 90 value 1679.683030
## iter 100 value 1563.678027
## iter 110 value 1545.352535
## iter 120 value 1526.578521
## iter 130 value 1515.343169
## iter 140 value 1506.192877
## iter 150 value 1498.975117
## iter 160 value 1490.369460
## iter 170 value 1487.801718
## iter 180 value 1485.897615
## iter 190 value 1485.775181
## iter 200 value 1485.650531
## iter 210 value 1485.501996
## iter 220 value 1485.497904
## iter 230 value 1485.484835
## final value 1485.484739
## converged
## # weights: 71
## initial value 1371779.205282
## iter 10 value 2041.259453
## iter 20 value 1180.730791
## iter 30 value 965.307145
## iter 40 value 896.110973
## iter 50 value 853.485688
## iter 60 value 830.740975
## iter 70 value 786.858172
## iter 80 value 758.653251
## iter 90 value 736.067309
## iter 100 value 696.525942
## iter 110 value 682.682772
## iter 120 value 668.194406
## iter 130 value 658.446248
## iter 140 value 652.499347
## iter 150 value 647.391144
## iter 160 value 644.940752
## iter 170 value 639.861777
## iter 180 value 631.223875
## iter 190 value 620.480615
## iter 200 value 609.807272
## iter 210 value 592.550789
## iter 220 value 577.476706
## iter 230 value 562.064976
## iter 240 value 553.001549
## iter 250 value 551.388826
## iter 260 value 550.354304
## iter 270 value 547.266167
## iter 280 value 543.700049
## iter 290 value 541.048983
## iter 300 value 540.807648
## iter 310 value 539.926757
## iter 320 value 539.513071
## iter 330 value 539.051649
## iter 340 value 537.980287
## iter 350 value 536.659244
## iter 360 value 534.851035
## iter 370 value 533.427602
## iter 380 value 532.381938
## iter 390 value 531.378447
## iter 400 value 531.114151
## iter 410 value 530.845823
## iter 420 value 529.984520
## iter 430 value 529.499983
## iter 440 value 529.442417
## iter 450 value 529.390786
## iter 460 value 529.339436
## iter 470 value 529.313635
## iter 480 value 529.283218
## iter 490 value 529.256925
## iter 500 value 529.176818
## final value 529.176818
## stopped after 500 iterations
## # weights: 106
## initial value 1389419.598289
## iter 10 value 1864.740434
## iter 20 value 1103.814917
## iter 30 value 899.655533
## iter 40 value 826.906983
## iter 50 value 782.069824
## iter 60 value 709.767414
## iter 70 value 652.485898
## iter 80 value 609.957881
## iter 90 value 556.684149
## iter 100 value 521.057830
## iter 110 value 495.432430
## iter 120 value 478.974511
## iter 130 value 452.402426
## iter 140 value 432.842799
## iter 150 value 421.762568
## iter 160 value 416.535023
## iter 170 value 412.628814
## iter 180 value 404.223314
## iter 190 value 390.251542
## iter 200 value 377.633514
## iter 210 value 365.780165
## iter 220 value 360.538325
## iter 230 value 358.682043
## iter 240 value 355.502192
## iter 250 value 352.300711
## iter 260 value 349.395695
## iter 270 value 345.029110
## iter 280 value 337.899434
## iter 290 value 334.029557
## iter 300 value 330.642420
## iter 310 value 325.184476
## iter 320 value 322.187385
## iter 330 value 319.863822
## iter 340 value 318.644723
## iter 350 value 316.897860
## iter 360 value 316.045842
## iter 370 value 314.304491
## iter 380 value 313.551632
## iter 390 value 312.206192
## iter 400 value 310.259822
## iter 410 value 309.724034
## iter 420 value 309.610816
## iter 430 value 309.584429
## iter 440 value 309.582305
## iter 450 value 309.576328
## iter 460 value 309.569959
## iter 470 value 309.565284
## iter 480 value 309.563138
## iter 490 value 309.560861
## iter 500 value 309.554007
## final value 309.554007
## stopped after 500 iterations
## # weights: 141
## initial value 1398016.686222
## iter 10 value 1565.105950
## iter 20 value 1099.277338
## iter 30 value 899.889456
## iter 40 value 778.738743
## iter 50 value 699.333858
## iter 60 value 622.568080
## iter 70 value 534.120331
## iter 80 value 475.671982
## iter 90 value 444.124394
## iter 100 value 420.785442
## iter 110 value 404.554371
## iter 120 value 380.880265
## iter 130 value 356.425700
## iter 140 value 338.182778
## iter 150 value 321.201687
## iter 160 value 312.942894
## iter 170 value 297.639043
## iter 180 value 278.335166
## iter 190 value 267.868917
## iter 200 value 259.408266
## iter 210 value 253.633228
## iter 220 value 235.519085
## iter 230 value 229.508877
## iter 240 value 225.124058
## iter 250 value 218.562371
## iter 260 value 212.662072
## iter 270 value 207.902502
## iter 280 value 201.673356
## iter 290 value 199.623885
## iter 300 value 198.491262
## iter 310 value 195.968982
## iter 320 value 193.987561
## iter 330 value 191.419691
## iter 340 value 189.416765
## iter 350 value 187.937937
## iter 360 value 186.007531
## iter 370 value 182.269374
## iter 380 value 179.049133
## iter 390 value 173.622423
## iter 400 value 171.299692
## iter 410 value 168.012696
## iter 420 value 164.550174
## iter 430 value 162.301052
## iter 440 value 161.243032
## iter 450 value 160.126985
## iter 460 value 158.845396
## iter 470 value 157.202095
## iter 480 value 155.260796
## iter 490 value 153.961776
## iter 500 value 152.949283
## final value 152.949283
## stopped after 500 iterations
## # weights: 15
## initial value 1427199.364592
## iter 10 value 3242.688199
## iter 20 value 2249.407741
## iter 30 value 1595.837381
## iter 40 value 1557.964498
## iter 50 value 1491.031424
## iter 60 value 1457.539530
## iter 70 value 1448.962899
## iter 80 value 1442.410842
## iter 90 value 1426.997669
## iter 100 value 1421.633111
## iter 110 value 1420.373954
## iter 120 value 1418.315760
## iter 130 value 1416.125748
## iter 140 value 1415.880539
## iter 150 value 1413.615922
## iter 160 value 1412.632274
## iter 170 value 1412.410624
## iter 180 value 1411.948765
## iter 190 value 1410.971972
## iter 200 value 1410.942898
## iter 210 value 1410.518671
## iter 220 value 1409.852664
## iter 230 value 1409.831473
## iter 240 value 1409.618982
## iter 250 value 1409.483457
## iter 260 value 1409.461759
## iter 270 value 1409.152783
## iter 280 value 1409.043085
## iter 290 value 1408.993616
## iter 300 value 1408.822682
## iter 310 value 1408.784378
## iter 320 value 1408.776997
## iter 330 value 1408.658837
## iter 340 value 1408.571740
## iter 350 value 1408.546493
## iter 360 value 1408.465372
## iter 370 value 1408.393801
## final value 1408.382644
## converged
## # weights: 36
## initial value 1387443.119112
## iter 10 value 5029.065347
## iter 20 value 2912.972548
## iter 30 value 2237.542984
## iter 40 value 2006.347229
## iter 50 value 1909.375173
## iter 60 value 1790.946282
## iter 70 value 1712.613145
## iter 80 value 1681.725961
## iter 90 value 1599.311611
## iter 100 value 1516.571936
## iter 110 value 1422.507640
## iter 120 value 1346.716619
## iter 130 value 1291.638892
## iter 140 value 1271.103397
## iter 150 value 1258.578434
## iter 160 value 1253.209060
## iter 170 value 1248.402212
## iter 180 value 1245.130798
## iter 190 value 1243.481291
## iter 200 value 1241.810653
## iter 210 value 1239.466220
## iter 220 value 1238.353674
## iter 230 value 1237.834079
## iter 240 value 1237.643670
## iter 240 value 1237.643663
## final value 1237.643421
## converged
## # weights: 71
## initial value 1360784.265008
## iter 10 value 2446.221325
## iter 20 value 1437.744290
## iter 30 value 1185.414210
## iter 40 value 1088.668630
## iter 50 value 1052.982109
## iter 60 value 1005.474316
## iter 70 value 986.691240
## iter 80 value 959.294830
## iter 90 value 903.219676
## iter 100 value 859.172852
## iter 110 value 836.883717
## iter 120 value 809.661391
## iter 130 value 799.897828
## iter 140 value 790.158781
## iter 150 value 780.159581
## iter 160 value 775.703920
## iter 170 value 767.937968
## iter 180 value 759.076911
## iter 190 value 744.712439
## iter 200 value 726.770310
## iter 210 value 699.079043
## iter 220 value 682.734124
## iter 230 value 672.577403
## iter 240 value 666.041811
## iter 250 value 658.411027
## iter 260 value 655.067148
## iter 270 value 652.181160
## iter 280 value 650.666221
## iter 290 value 650.035753
## iter 300 value 649.809694
## iter 310 value 649.175119
## iter 320 value 648.216135
## iter 330 value 647.233034
## iter 340 value 645.887567
## iter 350 value 645.033747
## iter 360 value 644.218599
## iter 370 value 643.494702
## iter 380 value 641.894373
## iter 390 value 630.928016
## iter 400 value 618.027459
## iter 410 value 611.767345
## iter 420 value 605.466233
## iter 430 value 601.173379
## iter 440 value 600.948404
## iter 450 value 600.047340
## iter 460 value 599.613169
## iter 470 value 597.130243
## iter 480 value 592.026027
## iter 490 value 584.257597
## iter 500 value 574.192437
## final value 574.192437
## stopped after 500 iterations
## # weights: 106
## initial value 1366264.493128
## iter 10 value 1797.543543
## iter 20 value 1106.069376
## iter 30 value 974.949719
## iter 40 value 881.972848
## iter 50 value 793.602851
## iter 60 value 746.691279
## iter 70 value 696.980319
## iter 80 value 643.653277
## iter 90 value 597.506944
## iter 100 value 566.416881
## iter 110 value 535.680188
## iter 120 value 495.313494
## iter 130 value 481.670133
## iter 140 value 462.008820
## iter 150 value 442.253220
## iter 160 value 421.758974
## iter 170 value 408.823868
## iter 180 value 394.950045
## iter 190 value 386.101005
## iter 200 value 372.902567
## iter 210 value 367.537189
## iter 220 value 365.113668
## iter 230 value 363.852430
## iter 240 value 362.057798
## iter 250 value 359.882270
## iter 260 value 357.754747
## iter 270 value 354.472726
## iter 280 value 350.999064
## iter 290 value 345.225527
## iter 300 value 335.930593
## iter 310 value 327.823448
## iter 320 value 322.815772
## iter 330 value 320.863011
## iter 340 value 319.408929
## iter 350 value 318.297116
## iter 360 value 317.594408
## iter 370 value 317.177555
## iter 380 value 316.666621
## iter 390 value 315.990469
## iter 400 value 315.371832
## iter 410 value 314.658312
## iter 420 value 313.700616
## iter 430 value 312.902312
## iter 440 value 312.864354
## iter 450 value 312.796909
## iter 460 value 312.631537
## iter 470 value 312.441780
## iter 480 value 312.224011
## iter 490 value 311.993086
## iter 500 value 311.869517
## final value 311.869517
## stopped after 500 iterations
## # weights: 141
## initial value 1372533.323581
## iter 10 value 1480.367520
## iter 20 value 1126.726901
## iter 30 value 988.676507
## iter 40 value 840.096714
## iter 50 value 762.684788
## iter 60 value 664.892252
## iter 70 value 633.842942
## iter 80 value 594.420480
## iter 90 value 564.531276
## iter 100 value 525.018027
## iter 110 value 501.725913
## iter 120 value 481.878288
## iter 130 value 467.392250
## iter 140 value 448.444260
## iter 150 value 437.807810
## iter 160 value 423.531165
## iter 170 value 406.767804
## iter 180 value 381.567118
## iter 190 value 363.032250
## iter 200 value 348.566988
## iter 210 value 335.391371
## iter 220 value 322.134637
## iter 230 value 312.566423
## iter 240 value 285.064343
## iter 250 value 256.470562
## iter 260 value 244.150241
## iter 270 value 239.503331
## iter 280 value 236.672330
## iter 290 value 235.262732
## iter 300 value 234.355997
## iter 310 value 232.702594
## iter 320 value 230.556446
## iter 330 value 226.494332
## iter 340 value 222.311119
## iter 350 value 219.220469
## iter 360 value 217.238618
## iter 370 value 213.503818
## iter 380 value 205.421389
## iter 390 value 200.221458
## iter 400 value 197.021718
## iter 410 value 192.395241
## iter 420 value 184.411012
## iter 430 value 180.493921
## iter 440 value 178.251529
## iter 450 value 177.501438
## iter 460 value 177.168799
## iter 470 value 176.316968
## iter 480 value 174.794190
## iter 490 value 173.118031
## iter 500 value 171.821966
## final value 171.821966
## stopped after 500 iterations
## # weights: 15
## initial value 1400219.291998
## iter 10 value 16325.030064
## iter 20 value 15066.481578
## iter 30 value 12981.858867
## iter 40 value 4474.582093
## iter 50 value 2971.566720
## iter 60 value 2575.484846
## iter 70 value 2028.998508
## iter 80 value 1925.122545
## iter 90 value 1923.681091
## final value 1923.234613
## converged
## # weights: 36
## initial value 1431727.198108
## iter 10 value 8284.986412
## iter 20 value 5295.495445
## iter 30 value 4118.975282
## iter 40 value 2633.104348
## iter 50 value 1940.944050
## iter 60 value 1679.685953
## iter 70 value 1598.626483
## iter 80 value 1507.322957
## iter 90 value 1438.811629
## iter 100 value 1419.836307
## iter 110 value 1376.251878
## iter 120 value 1325.691046
## iter 130 value 1289.892434
## iter 140 value 1281.453328
## iter 150 value 1278.971264
## iter 160 value 1278.561145
## final value 1278.560941
## converged
## # weights: 71
## initial value 1379117.849918
## iter 10 value 2989.077526
## iter 20 value 1955.114958
## iter 30 value 1633.446156
## iter 40 value 1424.928873
## iter 50 value 1370.391419
## iter 60 value 1311.298776
## iter 70 value 1275.096579
## iter 80 value 1227.828832
## iter 90 value 1178.794754
## iter 100 value 1138.618479
## iter 110 value 1115.821019
## iter 120 value 1092.705933
## iter 130 value 1039.559949
## iter 140 value 1003.059109
## iter 150 value 991.834398
## iter 160 value 983.513802
## iter 170 value 975.648985
## iter 180 value 970.955511
## iter 190 value 967.587718
## iter 200 value 955.113470
## iter 210 value 938.152614
## iter 220 value 923.314055
## iter 230 value 919.603360
## iter 240 value 918.475428
## iter 250 value 918.446634
## iter 260 value 918.443057
## final value 918.442995
## converged
## # weights: 106
## initial value 1410758.266674
## iter 10 value 2535.453940
## iter 20 value 1505.396895
## iter 30 value 1210.490180
## iter 40 value 1105.520629
## iter 50 value 1011.228998
## iter 60 value 933.757222
## iter 70 value 903.586133
## iter 80 value 891.623376
## iter 90 value 878.391404
## iter 100 value 863.905128
## iter 110 value 852.119581
## iter 120 value 836.922741
## iter 130 value 822.923312
## iter 140 value 805.659807
## iter 150 value 793.258493
## iter 160 value 782.291004
## iter 170 value 775.937624
## iter 180 value 767.753979
## iter 190 value 762.703005
## iter 200 value 749.728469
## iter 210 value 744.772040
## iter 220 value 740.534786
## iter 230 value 735.307256
## iter 240 value 729.975623
## iter 250 value 726.056542
## iter 260 value 721.853876
## iter 270 value 717.210090
## iter 280 value 710.256634
## iter 290 value 706.554727
## iter 300 value 703.789616
## iter 310 value 702.364107
## iter 320 value 700.224333
## iter 330 value 699.050215
## iter 340 value 697.660589
## iter 350 value 694.039316
## iter 360 value 692.655964
## iter 370 value 692.407043
## iter 380 value 692.306624
## iter 390 value 692.279008
## iter 400 value 692.266105
## iter 410 value 692.260321
## final value 692.259869
## converged
## # weights: 141
## initial value 1391336.697576
## iter 10 value 1679.134965
## iter 20 value 1160.969209
## iter 30 value 1012.610725
## iter 40 value 906.566783
## iter 50 value 838.487991
## iter 60 value 797.421465
## iter 70 value 764.611789
## iter 80 value 715.557849
## iter 90 value 692.928984
## iter 100 value 680.303625
## iter 110 value 674.730589
## iter 120 value 665.497172
## iter 130 value 645.807107
## iter 140 value 626.233835
## iter 150 value 615.015905
## iter 160 value 608.383590
## iter 170 value 603.948516
## iter 180 value 599.559590
## iter 190 value 595.078801
## iter 200 value 587.977366
## iter 210 value 581.186089
## iter 220 value 572.420636
## iter 230 value 569.002586
## iter 240 value 567.395920
## iter 250 value 566.056267
## iter 260 value 565.253453
## iter 270 value 564.491671
## iter 280 value 563.705579
## iter 290 value 563.256366
## iter 300 value 562.719545
## iter 310 value 561.778606
## iter 320 value 561.312043
## iter 330 value 561.071028
## iter 340 value 560.867456
## iter 350 value 560.582783
## iter 360 value 560.216370
## iter 370 value 559.260623
## iter 380 value 557.929919
## iter 390 value 556.965646
## iter 400 value 556.441930
## iter 410 value 555.893961
## iter 420 value 554.017782
## iter 430 value 551.394580
## iter 440 value 548.713322
## iter 450 value 547.269798
## iter 460 value 546.723920
## iter 470 value 546.286213
## iter 480 value 546.238980
## iter 490 value 546.228318
## iter 500 value 546.227711
## final value 546.227711
## stopped after 500 iterations
## # weights: 15
## initial value 1404826.362613
## iter 10 value 32721.877973
## iter 20 value 6453.845876
## iter 30 value 5737.494193
## iter 40 value 4633.977981
## iter 50 value 3619.024104
## iter 60 value 2526.020037
## iter 70 value 1808.841350
## iter 80 value 1556.222365
## iter 90 value 1497.069115
## iter 100 value 1484.569417
## iter 110 value 1469.349787
## iter 120 value 1461.292695
## iter 130 value 1459.511442
## iter 140 value 1459.149414
## iter 150 value 1457.858074
## iter 160 value 1457.761467
## iter 170 value 1457.733183
## iter 180 value 1457.673060
## final value 1457.659457
## converged
## # weights: 36
## initial value 1444227.392543
## iter 10 value 5062.236456
## iter 20 value 4675.618592
## iter 30 value 3435.776310
## iter 40 value 2876.615324
## iter 50 value 1721.785709
## iter 60 value 1462.511986
## iter 70 value 1384.303676
## iter 80 value 1325.151246
## iter 90 value 1261.684157
## iter 100 value 1251.574189
## iter 110 value 1219.100979
## iter 120 value 1161.555827
## iter 130 value 1082.304591
## iter 140 value 1020.980104
## iter 150 value 980.983478
## iter 160 value 957.879852
## iter 170 value 950.836718
## iter 180 value 948.142159
## iter 190 value 940.976788
## iter 200 value 939.305717
## iter 210 value 927.920022
## iter 220 value 901.157306
## iter 230 value 895.205046
## iter 240 value 892.141878
## iter 250 value 891.946879
## iter 260 value 891.754949
## iter 270 value 891.748145
## iter 270 value 891.748140
## final value 891.748116
## converged
## # weights: 71
## initial value 1429826.480921
## iter 10 value 3908.022721
## iter 20 value 1904.190811
## iter 30 value 1511.543426
## iter 40 value 1215.663584
## iter 50 value 1015.831856
## iter 60 value 952.940472
## iter 70 value 925.690631
## iter 80 value 910.993328
## iter 90 value 887.954082
## iter 100 value 865.824288
## iter 110 value 846.571250
## iter 120 value 833.556379
## iter 130 value 825.822751
## iter 140 value 816.510487
## iter 150 value 806.228886
## iter 160 value 800.818405
## iter 170 value 792.894778
## iter 180 value 787.195638
## iter 190 value 784.054576
## iter 200 value 780.978011
## iter 210 value 778.992896
## iter 220 value 777.771203
## iter 230 value 777.018584
## iter 240 value 776.140884
## iter 250 value 776.005139
## iter 260 value 774.062682
## iter 270 value 771.044483
## iter 280 value 761.139693
## iter 290 value 753.227927
## iter 300 value 741.255093
## iter 310 value 734.128351
## iter 320 value 728.859687
## iter 330 value 727.273290
## iter 340 value 726.289155
## iter 350 value 726.033905
## iter 360 value 725.606873
## iter 370 value 725.099174
## iter 380 value 723.368673
## iter 390 value 722.471860
## iter 400 value 722.172141
## iter 410 value 721.683755
## iter 420 value 720.940501
## iter 430 value 719.776472
## iter 440 value 718.354904
## iter 450 value 715.993559
## iter 460 value 714.307419
## iter 470 value 711.366624
## iter 480 value 704.823333
## iter 490 value 701.187206
## iter 500 value 699.438202
## final value 699.438202
## stopped after 500 iterations
## # weights: 106
## initial value 1395679.369946
## iter 10 value 1497.985843
## iter 20 value 1108.336926
## iter 30 value 951.659517
## iter 40 value 851.905880
## iter 50 value 768.214187
## iter 60 value 691.017469
## iter 70 value 633.929690
## iter 80 value 608.907073
## iter 90 value 582.681975
## iter 100 value 556.747459
## iter 110 value 536.767086
## iter 120 value 524.995917
## iter 130 value 514.789354
## iter 140 value 504.574917
## iter 150 value 487.104922
## iter 160 value 476.344342
## iter 170 value 465.535386
## iter 180 value 455.164252
## iter 190 value 445.192897
## iter 200 value 441.116022
## iter 210 value 439.150846
## iter 220 value 437.836622
## iter 230 value 437.171045
## iter 240 value 434.934696
## iter 250 value 432.346010
## iter 260 value 429.801991
## iter 270 value 427.981366
## iter 280 value 419.640661
## iter 290 value 403.940257
## iter 300 value 395.876854
## iter 310 value 386.488723
## iter 320 value 381.275499
## iter 330 value 377.688796
## iter 340 value 371.857174
## iter 350 value 366.901524
## iter 360 value 360.397980
## iter 370 value 355.648910
## iter 380 value 354.022210
## iter 390 value 353.280329
## iter 400 value 353.023956
## iter 410 value 352.872002
## iter 420 value 352.743042
## iter 430 value 352.657504
## iter 440 value 352.649331
## iter 450 value 352.634584
## iter 460 value 352.597679
## iter 470 value 352.529372
## iter 480 value 352.464494
## iter 490 value 352.423608
## iter 500 value 352.390627
## final value 352.390627
## stopped after 500 iterations
## # weights: 141
## initial value 1402396.474510
## iter 10 value 1565.384025
## iter 20 value 1095.466559
## iter 30 value 911.356603
## iter 40 value 812.853393
## iter 50 value 732.232344
## iter 60 value 665.504817
## iter 70 value 601.970845
## iter 80 value 527.318422
## iter 90 value 471.846724
## iter 100 value 435.855865
## iter 110 value 402.716331
## iter 120 value 368.427592
## iter 130 value 339.663207
## iter 140 value 318.397535
## iter 150 value 308.567002
## iter 160 value 298.716965
## iter 170 value 289.773355
## iter 180 value 279.397730
## iter 190 value 270.094790
## iter 200 value 262.558848
## iter 210 value 256.933785
## iter 220 value 248.609932
## iter 230 value 240.671532
## iter 240 value 235.995528
## iter 250 value 231.408183
## iter 260 value 229.583538
## iter 270 value 224.923008
## iter 280 value 221.256600
## iter 290 value 220.228144
## iter 300 value 219.521360
## iter 310 value 218.156805
## iter 320 value 214.315702
## iter 330 value 210.610226
## iter 340 value 208.201640
## iter 350 value 206.429068
## iter 360 value 205.146683
## iter 370 value 204.094262
## iter 380 value 203.192205
## iter 390 value 201.312009
## iter 400 value 196.900440
## iter 410 value 195.181317
## iter 420 value 193.423232
## iter 430 value 192.595965
## iter 440 value 191.757948
## iter 450 value 191.145245
## iter 460 value 190.919524
## iter 470 value 190.849527
## iter 480 value 190.817376
## iter 490 value 190.797801
## iter 500 value 190.786654
## final value 190.786654
## stopped after 500 iterations
## # weights: 15
## initial value 1451473.195072
## iter 10 value 9999.874955
## iter 20 value 6284.155426
## iter 30 value 3408.293275
## iter 40 value 2346.660738
## iter 50 value 1861.534597
## iter 60 value 1804.594349
## iter 70 value 1797.358378
## iter 80 value 1787.590490
## iter 90 value 1779.595845
## iter 100 value 1764.417013
## iter 110 value 1742.360651
## iter 120 value 1680.786733
## iter 130 value 1581.952025
## iter 140 value 1560.219304
## iter 150 value 1532.758315
## iter 160 value 1526.297509
## iter 170 value 1525.765720
## iter 180 value 1524.909073
## iter 190 value 1523.339970
## iter 200 value 1523.247658
## final value 1523.247106
## converged
## # weights: 36
## initial value 1426398.049107
## iter 10 value 48432.755975
## iter 20 value 6519.976709
## iter 30 value 5381.745208
## iter 40 value 3391.785638
## iter 50 value 2359.933558
## iter 60 value 1951.955342
## iter 70 value 1638.893474
## iter 80 value 1544.165259
## iter 90 value 1368.390150
## iter 100 value 1205.954665
## iter 110 value 1153.346121
## iter 120 value 1105.196885
## iter 130 value 1079.219723
## iter 140 value 1060.185847
## iter 150 value 1057.518592
## iter 160 value 1054.773903
## iter 170 value 1044.352646
## iter 180 value 1027.110759
## iter 190 value 1018.048185
## iter 200 value 1013.520825
## iter 210 value 1009.233565
## iter 220 value 1006.930774
## iter 230 value 1006.877489
## iter 240 value 1005.724700
## iter 250 value 1004.701416
## iter 260 value 1002.664993
## iter 270 value 1001.475816
## iter 280 value 1001.002681
## iter 290 value 994.731485
## iter 300 value 993.200207
## iter 310 value 992.949445
## iter 320 value 992.477898
## iter 330 value 991.727619
## iter 340 value 991.567608
## iter 350 value 991.543638
## iter 360 value 991.414729
## iter 370 value 991.313927
## iter 380 value 990.812654
## iter 390 value 990.350406
## iter 400 value 990.296293
## iter 410 value 990.221590
## iter 420 value 990.212721
## iter 430 value 990.195420
## iter 440 value 990.189898
## iter 450 value 990.165524
## iter 460 value 990.126153
## iter 470 value 990.123638
## final value 990.118601
## converged
## # weights: 71
## initial value 1405972.882724
## iter 10 value 1915.326261
## iter 20 value 1194.196347
## iter 30 value 1118.476386
## iter 40 value 985.901710
## iter 50 value 919.250758
## iter 60 value 893.044280
## iter 70 value 881.790577
## iter 80 value 860.052497
## iter 90 value 809.760319
## iter 100 value 774.911884
## iter 110 value 755.643078
## iter 120 value 732.841669
## iter 130 value 719.510505
## iter 140 value 701.017700
## iter 150 value 692.489865
## iter 160 value 688.667336
## iter 170 value 682.649356
## iter 180 value 676.086047
## iter 190 value 671.327534
## iter 200 value 660.854934
## iter 210 value 653.604903
## iter 220 value 639.407987
## iter 230 value 636.521749
## iter 240 value 635.594748
## iter 250 value 635.178167
## iter 260 value 634.368150
## iter 270 value 633.777238
## iter 280 value 633.701321
## iter 290 value 633.526592
## iter 300 value 633.501663
## iter 310 value 633.466699
## iter 320 value 633.402470
## iter 330 value 632.969770
## iter 340 value 627.240871
## iter 350 value 608.010389
## iter 360 value 599.090730
## iter 370 value 597.674555
## iter 380 value 597.485534
## iter 390 value 597.215531
## iter 400 value 596.999744
## iter 410 value 596.658741
## iter 420 value 596.607103
## iter 430 value 596.534104
## iter 440 value 596.440549
## iter 450 value 596.013803
## iter 460 value 595.929490
## iter 470 value 595.849942
## iter 480 value 595.785580
## iter 490 value 595.736454
## iter 500 value 595.700857
## final value 595.700857
## stopped after 500 iterations
## # weights: 106
## initial value 1418359.832409
## iter 10 value 1452.786704
## iter 20 value 1036.847032
## iter 30 value 920.106690
## iter 40 value 833.317077
## iter 50 value 766.130133
## iter 60 value 722.293706
## iter 70 value 668.257294
## iter 80 value 636.712826
## iter 90 value 618.548626
## iter 100 value 598.051080
## iter 110 value 578.634890
## iter 120 value 544.075382
## iter 130 value 528.564691
## iter 140 value 515.171536
## iter 150 value 505.497297
## iter 160 value 490.666183
## iter 170 value 476.795460
## iter 180 value 464.203983
## iter 190 value 450.534716
## iter 200 value 442.057696
## iter 210 value 428.269413
## iter 220 value 415.716016
## iter 230 value 411.805372
## iter 240 value 400.500323
## iter 250 value 389.409199
## iter 260 value 377.573731
## iter 270 value 367.017041
## iter 280 value 354.721519
## iter 290 value 347.659602
## iter 300 value 341.802013
## iter 310 value 331.436035
## iter 320 value 326.980036
## iter 330 value 323.097620
## iter 340 value 321.484679
## iter 350 value 320.750515
## iter 360 value 320.357687
## iter 370 value 320.036586
## iter 380 value 319.386035
## iter 390 value 318.978826
## iter 400 value 318.632261
## iter 410 value 318.566028
## iter 420 value 318.398991
## iter 430 value 318.330713
## iter 440 value 318.325070
## iter 450 value 318.318671
## iter 460 value 318.314279
## iter 470 value 318.307885
## iter 480 value 318.298592
## iter 490 value 318.274969
## iter 500 value 318.251595
## final value 318.251595
## stopped after 500 iterations
## # weights: 141
## initial value 1432813.655953
## iter 10 value 2102.635737
## iter 20 value 1185.653353
## iter 30 value 936.188605
## iter 40 value 805.788597
## iter 50 value 728.498664
## iter 60 value 689.484378
## iter 70 value 636.591584
## iter 80 value 587.900354
## iter 90 value 550.708603
## iter 100 value 503.608683
## iter 110 value 477.530651
## iter 120 value 451.850853
## iter 130 value 425.495600
## iter 140 value 401.364566
## iter 150 value 375.137777
## iter 160 value 339.916821
## iter 170 value 317.178937
## iter 180 value 300.947840
## iter 190 value 290.725855
## iter 200 value 269.207375
## iter 210 value 253.732018
## iter 220 value 242.727055
## iter 230 value 234.262680
## iter 240 value 227.394285
## iter 250 value 223.483319
## iter 260 value 217.864359
## iter 270 value 214.152285
## iter 280 value 208.303563
## iter 290 value 205.782290
## iter 300 value 204.416298
## iter 310 value 202.398359
## iter 320 value 201.313649
## iter 330 value 199.420724
## iter 340 value 196.697899
## iter 350 value 194.264986
## iter 360 value 192.218276
## iter 370 value 188.909482
## iter 380 value 182.890105
## iter 390 value 179.637395
## iter 400 value 177.989207
## iter 410 value 174.742177
## iter 420 value 171.907411
## iter 430 value 169.385245
## iter 440 value 166.739346
## iter 450 value 164.619503
## iter 460 value 163.896160
## iter 470 value 163.223401
## iter 480 value 162.397472
## iter 490 value 162.182061
## iter 500 value 161.988318
## final value 161.988318
## stopped after 500 iterations
## # weights: 15
## initial value 1390034.197438
## iter 10 value 13892.124029
## iter 20 value 6562.188313
## iter 30 value 3051.444410
## iter 40 value 2127.421799
## iter 50 value 1876.204600
## iter 60 value 1826.133665
## iter 70 value 1802.185099
## iter 80 value 1791.544467
## iter 90 value 1787.494721
## iter 100 value 1785.775576
## iter 110 value 1783.312243
## iter 120 value 1782.484121
## final value 1782.483480
## converged
## # weights: 36
## initial value 1366184.001165
## iter 10 value 6654.551523
## iter 20 value 3417.026801
## iter 30 value 3157.240097
## iter 40 value 3043.722350
## iter 50 value 2927.381630
## iter 60 value 2812.134864
## iter 70 value 2226.318858
## iter 80 value 1869.583723
## iter 90 value 1703.540293
## iter 100 value 1581.067435
## iter 110 value 1507.632134
## iter 120 value 1477.089366
## iter 130 value 1448.334885
## iter 140 value 1432.439887
## iter 150 value 1422.224080
## iter 160 value 1411.459191
## iter 170 value 1408.426088
## iter 180 value 1402.467856
## iter 190 value 1398.844598
## iter 200 value 1392.989529
## iter 210 value 1392.297878
## iter 220 value 1392.110154
## iter 230 value 1391.997603
## iter 240 value 1391.940292
## iter 250 value 1391.821173
## iter 260 value 1391.703381
## iter 270 value 1391.586220
## iter 280 value 1391.563831
## iter 290 value 1391.550603
## iter 300 value 1391.537559
## iter 310 value 1391.505456
## iter 320 value 1391.495127
## iter 330 value 1391.477459
## iter 340 value 1391.450899
## final value 1391.443955
## converged
## # weights: 71
## initial value 1391813.114842
## iter 10 value 2657.100294
## iter 20 value 1634.379497
## iter 30 value 1252.532240
## iter 40 value 1131.097605
## iter 50 value 1059.148725
## iter 60 value 1021.456656
## iter 70 value 979.072678
## iter 80 value 922.713984
## iter 90 value 877.089914
## iter 100 value 833.580205
## iter 110 value 807.856865
## iter 120 value 786.434591
## iter 130 value 766.468394
## iter 140 value 755.635622
## iter 150 value 750.615626
## iter 160 value 748.927547
## iter 170 value 743.641113
## iter 180 value 723.671557
## iter 190 value 676.136056
## iter 200 value 646.064332
## iter 210 value 630.868904
## iter 220 value 628.582884
## iter 230 value 627.673696
## iter 240 value 627.206151
## iter 250 value 627.093884
## iter 260 value 627.035724
## iter 270 value 626.930539
## iter 280 value 626.799097
## iter 290 value 626.733688
## iter 300 value 626.720008
## iter 310 value 626.701682
## iter 320 value 626.682585
## iter 330 value 626.655762
## iter 340 value 626.640426
## iter 350 value 626.611153
## iter 360 value 626.581425
## iter 370 value 626.492494
## iter 380 value 626.297004
## iter 390 value 626.197880
## iter 400 value 626.142191
## iter 410 value 626.118176
## iter 420 value 626.110193
## iter 430 value 626.108231
## iter 430 value 626.108231
## final value 626.108231
## converged
## # weights: 106
## initial value 1347737.515088
## iter 10 value 1456.152890
## iter 20 value 1051.910948
## iter 30 value 957.944274
## iter 40 value 869.638904
## iter 50 value 803.947749
## iter 60 value 778.208427
## iter 70 value 755.243332
## iter 80 value 733.766593
## iter 90 value 712.589748
## iter 100 value 687.868965
## iter 110 value 659.591066
## iter 120 value 637.406535
## iter 130 value 620.310398
## iter 140 value 607.039906
## iter 150 value 590.667537
## iter 160 value 567.129722
## iter 170 value 548.984706
## iter 180 value 531.821781
## iter 190 value 524.878809
## iter 200 value 512.113325
## iter 210 value 500.144779
## iter 220 value 494.540525
## iter 230 value 491.317886
## iter 240 value 484.234859
## iter 250 value 477.131535
## iter 260 value 472.703976
## iter 270 value 470.226546
## iter 280 value 463.632302
## iter 290 value 457.317511
## iter 300 value 447.362587
## iter 310 value 442.759559
## iter 320 value 438.021809
## iter 330 value 434.094670
## iter 340 value 432.777422
## iter 350 value 431.375867
## iter 360 value 430.276205
## iter 370 value 429.770074
## iter 380 value 428.890657
## iter 390 value 427.135000
## iter 400 value 424.457959
## iter 410 value 421.697228
## iter 420 value 420.369075
## iter 430 value 419.803702
## iter 440 value 419.708702
## iter 450 value 419.599151
## iter 460 value 419.418681
## iter 470 value 418.998346
## iter 480 value 416.298606
## iter 490 value 413.662347
## iter 500 value 411.517987
## final value 411.517987
## stopped after 500 iterations
## # weights: 141
## initial value 1420653.045091
## iter 10 value 1711.164326
## iter 20 value 1110.907664
## iter 30 value 974.478876
## iter 40 value 877.348897
## iter 50 value 782.091139
## iter 60 value 679.990586
## iter 70 value 626.639526
## iter 80 value 557.541007
## iter 90 value 528.641102
## iter 100 value 498.316283
## iter 110 value 467.256378
## iter 120 value 439.128405
## iter 130 value 417.336519
## iter 140 value 387.636253
## iter 150 value 350.199237
## iter 160 value 316.187138
## iter 170 value 289.024498
## iter 180 value 268.293152
## iter 190 value 254.327908
## iter 200 value 242.466847
## iter 210 value 233.942305
## iter 220 value 221.951709
## iter 230 value 216.097933
## iter 240 value 209.986860
## iter 250 value 203.126497
## iter 260 value 198.046217
## iter 270 value 193.460140
## iter 280 value 190.334481
## iter 290 value 188.691913
## iter 300 value 187.153414
## iter 310 value 183.927521
## iter 320 value 180.992975
## iter 330 value 178.408392
## iter 340 value 175.608049
## iter 350 value 171.951506
## iter 360 value 169.115878
## iter 370 value 167.319167
## iter 380 value 165.323391
## iter 390 value 164.055264
## iter 400 value 161.973875
## iter 410 value 160.371288
## iter 420 value 158.622948
## iter 430 value 156.363277
## iter 440 value 154.606331
## iter 450 value 151.958988
## iter 460 value 148.972841
## iter 470 value 146.953390
## iter 480 value 145.769754
## iter 490 value 144.810802
## iter 500 value 144.295475
## final value 144.295475
## stopped after 500 iterations
## # weights: 15
## initial value 1424069.190252
## iter 10 value 6635.879391
## iter 20 value 5903.955599
## iter 30 value 5621.027754
## iter 40 value 5329.960127
## iter 50 value 5307.171748
## iter 60 value 5148.468381
## iter 70 value 3040.862786
## iter 80 value 2000.619272
## iter 90 value 1867.171755
## iter 100 value 1826.648976
## iter 110 value 1805.111883
## iter 120 value 1795.580194
## iter 130 value 1795.533810
## final value 1795.533624
## converged
## # weights: 36
## initial value 1443980.895185
## iter 10 value 7280.403142
## iter 20 value 4085.072536
## iter 30 value 2975.278007
## iter 40 value 1847.289436
## iter 50 value 1428.688958
## iter 60 value 1278.330422
## iter 70 value 1210.312262
## iter 80 value 1187.209394
## iter 90 value 1172.772049
## iter 100 value 1134.732123
## iter 110 value 1097.817867
## iter 120 value 1078.813174
## iter 130 value 1067.060421
## iter 140 value 1062.209518
## iter 150 value 1045.276789
## iter 160 value 1025.356024
## iter 170 value 1008.443855
## iter 180 value 1008.041913
## iter 190 value 1001.292018
## iter 200 value 993.480759
## iter 210 value 990.678807
## iter 220 value 987.734482
## iter 230 value 984.658447
## iter 240 value 982.672619
## iter 250 value 982.484801
## iter 260 value 982.388583
## iter 270 value 981.554356
## iter 280 value 981.399040
## iter 290 value 981.335401
## iter 300 value 981.137161
## iter 310 value 980.797101
## iter 320 value 980.779059
## iter 330 value 980.778186
## iter 340 value 980.749443
## iter 350 value 980.742110
## iter 360 value 980.733796
## iter 370 value 980.671317
## iter 380 value 980.602457
## iter 390 value 980.582106
## iter 400 value 980.581640
## final value 980.581611
## converged
## # weights: 71
## initial value 1374791.289966
## iter 10 value 4182.123958
## iter 20 value 2436.759607
## iter 30 value 1745.431098
## iter 40 value 1391.255354
## iter 50 value 1108.066002
## iter 60 value 943.730878
## iter 70 value 886.324368
## iter 80 value 825.999663
## iter 90 value 779.728138
## iter 100 value 755.351732
## iter 110 value 741.211483
## iter 120 value 734.594663
## iter 130 value 732.136351
## iter 140 value 729.832074
## iter 150 value 728.256347
## iter 160 value 727.810340
## iter 170 value 727.651120
## iter 180 value 727.607532
## iter 190 value 727.531994
## iter 200 value 727.506774
## iter 210 value 727.464843
## iter 220 value 727.438555
## iter 230 value 727.348023
## iter 240 value 727.248679
## iter 250 value 727.182550
## iter 260 value 727.144778
## iter 270 value 727.122134
## iter 280 value 727.109875
## iter 290 value 727.097412
## iter 300 value 727.089414
## iter 310 value 727.084121
## final value 727.084095
## converged
## # weights: 106
## initial value 1386376.518562
## iter 10 value 1247.294295
## iter 20 value 1038.179311
## iter 30 value 909.896658
## iter 40 value 823.815879
## iter 50 value 769.363055
## iter 60 value 720.898727
## iter 70 value 678.169966
## iter 80 value 647.781180
## iter 90 value 617.934369
## iter 100 value 563.129779
## iter 110 value 490.547015
## iter 120 value 429.173335
## iter 130 value 408.650063
## iter 140 value 377.261748
## iter 150 value 358.575244
## iter 160 value 346.642453
## iter 170 value 338.871959
## iter 180 value 334.002855
## iter 190 value 329.980320
## iter 200 value 327.466104
## iter 210 value 325.287960
## iter 220 value 323.993941
## iter 230 value 323.070865
## iter 240 value 321.718002
## iter 250 value 320.341605
## iter 260 value 319.149367
## iter 270 value 317.164050
## iter 280 value 315.816459
## iter 290 value 313.156483
## iter 300 value 309.147917
## iter 310 value 304.287366
## iter 320 value 300.633211
## iter 330 value 295.338694
## iter 340 value 292.412166
## iter 350 value 289.735876
## iter 360 value 287.979883
## iter 370 value 284.168822
## iter 380 value 272.178097
## iter 390 value 266.121787
## iter 400 value 264.037361
## iter 410 value 263.215245
## iter 420 value 261.608534
## iter 430 value 260.698217
## iter 440 value 260.617884
## iter 450 value 260.481716
## iter 460 value 260.410018
## iter 470 value 260.349893
## iter 480 value 260.074588
## iter 490 value 260.032616
## iter 500 value 259.955186
## final value 259.955186
## stopped after 500 iterations
## # weights: 141
## initial value 1409018.705112
## iter 10 value 1844.575466
## iter 20 value 1111.140329
## iter 30 value 964.658936
## iter 40 value 884.317005
## iter 50 value 776.252817
## iter 60 value 692.961189
## iter 70 value 633.867363
## iter 80 value 571.794127
## iter 90 value 522.391308
## iter 100 value 499.615265
## iter 110 value 476.708638
## iter 120 value 454.965722
## iter 130 value 435.481181
## iter 140 value 414.278938
## iter 150 value 385.732347
## iter 160 value 367.876911
## iter 170 value 347.448267
## iter 180 value 329.063970
## iter 190 value 309.370327
## iter 200 value 290.847554
## iter 210 value 278.631944
## iter 220 value 265.429359
## iter 230 value 253.150354
## iter 240 value 246.538322
## iter 250 value 241.199468
## iter 260 value 236.388921
## iter 270 value 233.155801
## iter 280 value 230.945762
## iter 290 value 229.819392
## iter 300 value 229.011413
## iter 310 value 228.007311
## iter 320 value 227.032619
## iter 330 value 226.056214
## iter 340 value 224.324973
## iter 350 value 221.525407
## iter 360 value 219.055554
## iter 370 value 216.455249
## iter 380 value 213.844314
## iter 390 value 209.720402
## iter 400 value 202.645145
## iter 410 value 197.949226
## iter 420 value 194.761329
## iter 430 value 192.530156
## iter 440 value 191.356093
## iter 450 value 190.046195
## iter 460 value 189.086656
## iter 470 value 188.074922
## iter 480 value 187.271616
## iter 490 value 186.690422
## iter 500 value 186.043633
## final value 186.043633
## stopped after 500 iterations
## # weights: 15
## initial value 1402720.581926
## iter 10 value 10320.611162
## iter 20 value 9033.891051
## iter 30 value 7754.065428
## iter 40 value 6072.915906
## iter 50 value 3738.458154
## iter 60 value 2320.873572
## iter 70 value 1797.419317
## iter 80 value 1616.992327
## iter 90 value 1589.411758
## iter 100 value 1542.523117
## iter 110 value 1515.046126
## iter 120 value 1510.328549
## iter 130 value 1509.538536
## final value 1509.505079
## converged
## # weights: 36
## initial value 1386194.489221
## iter 10 value 101875.460853
## iter 20 value 32959.671045
## iter 30 value 9987.939720
## iter 40 value 5698.298534
## iter 50 value 4283.741617
## iter 60 value 2501.632114
## iter 70 value 1937.359968
## iter 80 value 1552.409305
## iter 90 value 1393.179486
## iter 100 value 1339.420470
## iter 110 value 1311.804863
## iter 120 value 1283.072744
## iter 130 value 1216.184077
## iter 140 value 1182.339946
## iter 150 value 1178.101608
## iter 160 value 1177.042523
## iter 170 value 1176.411048
## iter 180 value 1175.705566
## iter 190 value 1175.403006
## iter 200 value 1174.711678
## iter 210 value 1173.689247
## iter 220 value 1172.612147
## iter 230 value 1172.159412
## iter 240 value 1172.013974
## final value 1172.010770
## converged
## # weights: 71
## initial value 1396053.569499
## iter 10 value 3348.408305
## iter 20 value 2030.413248
## iter 30 value 1668.129944
## iter 40 value 1464.036382
## iter 50 value 1398.572290
## iter 60 value 1308.307907
## iter 70 value 1245.618153
## iter 80 value 1198.630055
## iter 90 value 1172.895225
## iter 100 value 1105.224645
## iter 110 value 1064.487764
## iter 120 value 1042.163154
## iter 130 value 1030.645278
## iter 140 value 1024.109557
## iter 150 value 1021.947389
## iter 160 value 1017.164075
## iter 170 value 1013.411143
## iter 180 value 1011.925871
## iter 190 value 1011.391476
## iter 200 value 1011.095843
## iter 210 value 1010.946906
## iter 220 value 1008.553339
## iter 230 value 1006.690289
## iter 240 value 1006.566334
## final value 1006.560429
## converged
## # weights: 106
## initial value 1391516.432562
## iter 10 value 1651.167238
## iter 20 value 1251.083304
## iter 30 value 1115.024091
## iter 40 value 1004.709583
## iter 50 value 955.982378
## iter 60 value 916.445844
## iter 70 value 890.251077
## iter 80 value 870.628227
## iter 90 value 853.357247
## iter 100 value 839.758639
## iter 110 value 829.431755
## iter 120 value 822.898385
## iter 130 value 815.269611
## iter 140 value 800.052318
## iter 150 value 787.450236
## iter 160 value 773.064706
## iter 170 value 759.697076
## iter 180 value 752.004200
## iter 190 value 740.734903
## iter 200 value 730.634272
## iter 210 value 726.799258
## iter 220 value 724.198612
## iter 230 value 720.027947
## iter 240 value 711.241997
## iter 250 value 704.217090
## iter 260 value 696.400582
## iter 270 value 693.056135
## iter 280 value 690.672336
## iter 290 value 687.886481
## iter 300 value 683.845858
## iter 310 value 681.197153
## iter 320 value 679.005369
## iter 330 value 677.799764
## iter 340 value 676.728619
## iter 350 value 676.424643
## iter 360 value 676.394985
## iter 370 value 676.394244
## final value 676.394219
## converged
## # weights: 141
## initial value 1431966.362448
## iter 10 value 1490.706632
## iter 20 value 1209.389376
## iter 30 value 1086.612162
## iter 40 value 1021.204629
## iter 50 value 959.489318
## iter 60 value 890.834848
## iter 70 value 855.744224
## iter 80 value 829.290153
## iter 90 value 793.608643
## iter 100 value 768.599793
## iter 110 value 744.324212
## iter 120 value 726.997448
## iter 130 value 707.120376
## iter 140 value 689.025439
## iter 150 value 681.849741
## iter 160 value 672.184899
## iter 170 value 663.316771
## iter 180 value 655.752892
## iter 190 value 649.283134
## iter 200 value 641.346472
## iter 210 value 634.067406
## iter 220 value 628.673020
## iter 230 value 620.702521
## iter 240 value 612.467706
## iter 250 value 609.368916
## iter 260 value 604.927903
## iter 270 value 599.893364
## iter 280 value 596.409329
## iter 290 value 595.198555
## iter 300 value 593.794668
## iter 310 value 591.448377
## iter 320 value 589.881803
## iter 330 value 589.251423
## iter 340 value 588.660829
## iter 350 value 588.011147
## iter 360 value 587.094786
## iter 370 value 585.945002
## iter 380 value 583.215844
## iter 390 value 581.510017
## iter 400 value 581.068589
## iter 410 value 580.852576
## iter 420 value 580.803359
## iter 430 value 580.769404
## iter 440 value 580.762703
## iter 450 value 580.760563
## final value 580.760385
## converged
## # weights: 15
## initial value 1397002.019183
## iter 10 value 20629.923574
## iter 20 value 11027.841073
## iter 30 value 5856.981940
## iter 40 value 5142.641439
## iter 50 value 4209.857075
## iter 60 value 2361.599064
## iter 70 value 2064.151928
## iter 80 value 1847.053856
## iter 90 value 1823.885867
## iter 100 value 1814.551563
## iter 110 value 1804.181113
## iter 120 value 1800.767789
## iter 130 value 1799.632021
## iter 140 value 1799.219463
## iter 150 value 1794.636456
## iter 160 value 1773.915327
## iter 170 value 1681.441716
## iter 180 value 1659.948644
## final value 1659.423588
## converged
## # weights: 36
## initial value 1414096.251600
## iter 10 value 2709.437795
## iter 20 value 1788.897752
## iter 30 value 1391.267360
## iter 40 value 1223.880107
## iter 50 value 1144.054983
## iter 60 value 1098.565361
## iter 70 value 1087.314627
## iter 80 value 1084.105204
## iter 90 value 1081.465356
## iter 100 value 1073.438628
## iter 110 value 1065.763494
## iter 120 value 1039.704350
## iter 130 value 986.244055
## iter 140 value 970.564976
## iter 150 value 965.563762
## iter 160 value 963.367023
## iter 170 value 958.152862
## iter 180 value 945.289910
## iter 190 value 940.633947
## iter 200 value 940.321929
## iter 210 value 940.255243
## iter 220 value 940.253476
## iter 220 value 940.253476
## final value 940.253476
## converged
## # weights: 71
## initial value 1394253.632215
## iter 10 value 3200.628477
## iter 20 value 1735.099501
## iter 30 value 1322.297661
## iter 40 value 1099.320053
## iter 50 value 999.444518
## iter 60 value 920.008910
## iter 70 value 896.405903
## iter 80 value 872.327153
## iter 90 value 845.528394
## iter 100 value 807.287629
## iter 110 value 745.245386
## iter 120 value 717.940599
## iter 130 value 698.521190
## iter 140 value 692.137863
## iter 150 value 690.002982
## iter 160 value 688.491148
## iter 170 value 687.464151
## iter 180 value 685.611881
## iter 190 value 680.417343
## iter 200 value 675.012656
## iter 210 value 671.782331
## iter 220 value 671.287493
## iter 230 value 669.802195
## iter 240 value 665.191673
## iter 250 value 659.674633
## iter 260 value 653.609435
## iter 270 value 648.654302
## iter 280 value 647.029240
## iter 290 value 645.609802
## iter 300 value 645.188807
## iter 310 value 645.168340
## iter 320 value 645.165064
## final value 645.164719
## converged
## # weights: 106
## initial value 1415252.908137
## iter 10 value 1736.174153
## iter 20 value 1149.296464
## iter 30 value 911.536294
## iter 40 value 810.067270
## iter 50 value 752.509179
## iter 60 value 679.659254
## iter 70 value 626.473432
## iter 80 value 585.425804
## iter 90 value 548.181105
## iter 100 value 523.018442
## iter 110 value 505.674216
## iter 120 value 498.789073
## iter 130 value 492.719132
## iter 140 value 486.714055
## iter 150 value 477.469363
## iter 160 value 469.654197
## iter 170 value 459.895522
## iter 180 value 451.245040
## iter 190 value 447.538415
## iter 200 value 443.426293
## iter 210 value 440.581463
## iter 220 value 439.294453
## iter 230 value 438.385818
## iter 240 value 435.053121
## iter 250 value 429.012767
## iter 260 value 421.840405
## iter 270 value 418.507067
## iter 280 value 413.529762
## iter 290 value 399.861418
## iter 300 value 394.881998
## iter 310 value 391.752172
## iter 320 value 385.919022
## iter 330 value 382.142778
## iter 340 value 377.223991
## iter 350 value 370.636767
## iter 360 value 363.643239
## iter 370 value 361.159591
## iter 380 value 359.541919
## iter 390 value 358.772838
## iter 400 value 358.511118
## iter 410 value 358.411810
## iter 420 value 358.240310
## iter 430 value 358.095445
## iter 440 value 358.062397
## iter 450 value 358.019919
## iter 460 value 357.945672
## iter 470 value 357.866273
## iter 480 value 357.796127
## iter 490 value 357.621143
## iter 500 value 357.390823
## final value 357.390823
## stopped after 500 iterations
## # weights: 141
## initial value 1312625.836615
## iter 10 value 1413.188581
## iter 20 value 1121.974937
## iter 30 value 954.382988
## iter 40 value 859.442334
## iter 50 value 753.920929
## iter 60 value 688.836022
## iter 70 value 647.293677
## iter 80 value 605.205853
## iter 90 value 538.787800
## iter 100 value 483.570881
## iter 110 value 449.156990
## iter 120 value 419.509644
## iter 130 value 394.513559
## iter 140 value 379.894016
## iter 150 value 368.220254
## iter 160 value 361.684027
## iter 170 value 355.353624
## iter 180 value 347.444538
## iter 190 value 342.048100
## iter 200 value 334.999553
## iter 210 value 329.043016
## iter 220 value 321.844182
## iter 230 value 315.803421
## iter 240 value 310.175060
## iter 250 value 307.852459
## iter 260 value 304.620041
## iter 270 value 302.315840
## iter 280 value 300.639048
## iter 290 value 299.409899
## iter 300 value 298.711622
## iter 310 value 297.499185
## iter 320 value 295.866229
## iter 330 value 293.362930
## iter 340 value 288.647338
## iter 350 value 284.678824
## iter 360 value 279.715284
## iter 370 value 275.806177
## iter 380 value 272.317030
## iter 390 value 267.148309
## iter 400 value 262.518896
## iter 410 value 259.120087
## iter 420 value 255.959338
## iter 430 value 253.864496
## iter 440 value 252.479472
## iter 450 value 251.100340
## iter 460 value 249.150001
## iter 470 value 243.644885
## iter 480 value 241.255169
## iter 490 value 238.884555
## iter 500 value 237.091930
## final value 237.091930
## stopped after 500 iterations
## # weights: 15
## initial value 1413533.660184
## iter 10 value 4721.894089
## iter 20 value 3416.384627
## iter 30 value 1927.536574
## iter 40 value 1800.717388
## iter 50 value 1666.988726
## iter 60 value 1641.844646
## iter 70 value 1575.835349
## iter 80 value 1507.321630
## iter 90 value 1444.003188
## iter 100 value 1274.923782
## iter 110 value 1259.461330
## iter 120 value 1253.175657
## iter 130 value 1236.620574
## iter 140 value 1230.732785
## iter 150 value 1228.920349
## iter 160 value 1225.804320
## iter 170 value 1223.734911
## iter 180 value 1223.569297
## iter 190 value 1222.930499
## iter 200 value 1222.541861
## iter 210 value 1222.487218
## iter 220 value 1222.162071
## iter 230 value 1221.655654
## iter 240 value 1221.612406
## iter 250 value 1221.582362
## iter 260 value 1221.519695
## final value 1221.516965
## converged
## # weights: 36
## initial value 1392306.769012
## iter 10 value 3495.866684
## iter 20 value 2586.700688
## iter 30 value 2393.753491
## iter 40 value 2131.211720
## iter 50 value 2057.587791
## iter 60 value 2017.415093
## iter 70 value 1853.602011
## iter 80 value 1683.490499
## iter 90 value 1505.011661
## iter 100 value 1338.334097
## iter 110 value 1200.576569
## iter 120 value 1179.235350
## iter 130 value 1167.690896
## iter 140 value 1162.949527
## iter 150 value 1157.261885
## iter 160 value 1156.575128
## iter 170 value 1156.552972
## iter 180 value 1156.329713
## iter 190 value 1156.279126
## iter 200 value 1154.874673
## iter 210 value 1153.741750
## iter 220 value 1153.678035
## iter 230 value 1151.277530
## iter 240 value 1150.348107
## final value 1150.244051
## converged
## # weights: 71
## initial value 1430457.741955
## iter 10 value 2111.775832
## iter 20 value 1196.036583
## iter 30 value 1054.648346
## iter 40 value 952.624033
## iter 50 value 903.238783
## iter 60 value 857.466852
## iter 70 value 825.161480
## iter 80 value 791.499398
## iter 90 value 771.815535
## iter 100 value 740.380278
## iter 110 value 724.700888
## iter 120 value 715.402902
## iter 130 value 703.822034
## iter 140 value 692.671990
## iter 150 value 687.258316
## iter 160 value 683.371189
## iter 170 value 675.911088
## iter 180 value 670.068800
## iter 190 value 656.565668
## iter 200 value 645.747245
## iter 210 value 636.675883
## iter 220 value 630.720232
## iter 230 value 617.075232
## iter 240 value 608.103878
## iter 250 value 603.319656
## iter 260 value 601.952011
## iter 270 value 597.352497
## iter 280 value 594.656861
## iter 290 value 592.652158
## iter 300 value 590.060822
## iter 310 value 588.307095
## iter 320 value 584.803095
## iter 330 value 580.003906
## iter 340 value 578.884971
## iter 350 value 578.644450
## iter 360 value 578.040543
## iter 370 value 577.074206
## iter 380 value 573.990947
## iter 390 value 572.418554
## iter 400 value 572.002550
## iter 410 value 571.070268
## iter 420 value 570.537695
## iter 430 value 570.431509
## iter 440 value 570.045800
## iter 450 value 568.596114
## iter 460 value 566.745335
## iter 470 value 566.000581
## iter 480 value 564.856702
## iter 490 value 564.784314
## iter 500 value 564.616962
## final value 564.616962
## stopped after 500 iterations
## # weights: 106
## initial value 1461657.112234
## iter 10 value 1739.933571
## iter 20 value 1129.764524
## iter 30 value 944.472279
## iter 40 value 841.536783
## iter 50 value 790.010241
## iter 60 value 739.058525
## iter 70 value 700.215976
## iter 80 value 675.755498
## iter 90 value 653.998602
## iter 100 value 627.266987
## iter 110 value 606.752424
## iter 120 value 582.285879
## iter 130 value 564.673206
## iter 140 value 546.879996
## iter 150 value 529.985040
## iter 160 value 513.233184
## iter 170 value 498.716100
## iter 180 value 474.959332
## iter 190 value 458.375545
## iter 200 value 446.665097
## iter 210 value 432.422076
## iter 220 value 426.593379
## iter 230 value 424.377783
## iter 240 value 413.280879
## iter 250 value 405.589091
## iter 260 value 392.814315
## iter 270 value 383.194085
## iter 280 value 374.911029
## iter 290 value 364.397982
## iter 300 value 353.497896
## iter 310 value 342.664572
## iter 320 value 335.903219
## iter 330 value 332.966207
## iter 340 value 332.199813
## iter 350 value 331.649081
## iter 360 value 331.210729
## iter 370 value 330.898721
## iter 380 value 329.693599
## iter 390 value 327.573093
## iter 400 value 326.216246
## iter 410 value 325.977657
## iter 420 value 325.741942
## iter 430 value 325.608225
## iter 440 value 325.591001
## iter 450 value 325.558885
## iter 460 value 325.506084
## iter 470 value 325.411805
## iter 480 value 325.303592
## iter 490 value 325.192868
## iter 500 value 324.892144
## final value 324.892144
## stopped after 500 iterations
## # weights: 141
## initial value 1426004.080425
## iter 10 value 1657.822788
## iter 20 value 1208.709292
## iter 30 value 1029.073809
## iter 40 value 900.484445
## iter 50 value 783.050196
## iter 60 value 702.087542
## iter 70 value 645.425211
## iter 80 value 602.557742
## iter 90 value 537.711814
## iter 100 value 504.170713
## iter 110 value 471.346255
## iter 120 value 450.171713
## iter 130 value 430.162608
## iter 140 value 406.953514
## iter 150 value 389.325455
## iter 160 value 374.075461
## iter 170 value 363.409511
## iter 180 value 354.457831
## iter 190 value 340.870779
## iter 200 value 329.537819
## iter 210 value 310.796943
## iter 220 value 298.272530
## iter 230 value 287.306282
## iter 240 value 276.529411
## iter 250 value 266.324739
## iter 260 value 261.078707
## iter 270 value 255.930320
## iter 280 value 251.011927
## iter 290 value 248.903234
## iter 300 value 247.621877
## iter 310 value 246.093950
## iter 320 value 243.733781
## iter 330 value 240.558099
## iter 340 value 235.914055
## iter 350 value 231.871406
## iter 360 value 227.510080
## iter 370 value 224.205130
## iter 380 value 220.245469
## iter 390 value 212.807360
## iter 400 value 208.670687
## iter 410 value 206.763314
## iter 420 value 205.485329
## iter 430 value 203.864056
## iter 440 value 202.487586
## iter 450 value 200.880771
## iter 460 value 200.316046
## iter 470 value 199.921837
## iter 480 value 199.252679
## iter 490 value 197.999137
## iter 500 value 197.005525
## final value 197.005525
## stopped after 500 iterations
## # weights: 15
## initial value 1408432.479508
## iter 10 value 15171.895631
## iter 20 value 6792.528075
## iter 30 value 3106.481551
## iter 40 value 2605.690059
## iter 50 value 2042.786602
## iter 60 value 1713.140333
## iter 70 value 1696.961086
## iter 80 value 1660.309124
## iter 90 value 1645.545975
## iter 100 value 1643.237576
## iter 110 value 1641.871108
## iter 120 value 1635.572779
## iter 130 value 1633.842065
## iter 140 value 1633.714580
## iter 150 value 1631.474656
## iter 160 value 1629.589204
## iter 170 value 1629.567523
## iter 180 value 1628.106566
## iter 190 value 1627.055879
## iter 200 value 1626.921655
## iter 210 value 1626.429491
## iter 220 value 1625.880181
## iter 230 value 1625.851738
## iter 240 value 1625.837352
## iter 250 value 1625.595330
## iter 260 value 1625.500214
## final value 1625.500173
## converged
## # weights: 36
## initial value 1424448.457589
## iter 10 value 4575.681223
## iter 20 value 2686.100236
## iter 30 value 2247.465147
## iter 40 value 1782.994432
## iter 50 value 1403.011423
## iter 60 value 1247.919580
## iter 70 value 1222.091882
## iter 80 value 1208.664119
## iter 90 value 1202.077338
## iter 100 value 1186.449513
## iter 110 value 1171.599896
## iter 120 value 1167.843354
## iter 130 value 1163.283684
## iter 140 value 1160.881349
## iter 150 value 1160.530210
## iter 160 value 1160.500446
## iter 170 value 1160.395427
## iter 180 value 1160.271370
## iter 190 value 1160.179868
## iter 200 value 1160.148109
## iter 210 value 1160.000179
## iter 220 value 1159.491818
## iter 230 value 1159.264864
## iter 240 value 1158.627279
## iter 250 value 1155.722644
## iter 260 value 1154.730701
## iter 270 value 1154.647463
## iter 280 value 1154.535382
## iter 290 value 1154.498254
## iter 300 value 1154.488706
## iter 310 value 1154.488246
## final value 1154.488196
## converged
## # weights: 71
## initial value 1417557.681487
## iter 10 value 2080.368532
## iter 20 value 1381.057850
## iter 30 value 1142.244345
## iter 40 value 934.010545
## iter 50 value 850.350536
## iter 60 value 797.620170
## iter 70 value 767.714937
## iter 80 value 738.635854
## iter 90 value 720.785523
## iter 100 value 679.792567
## iter 110 value 656.579060
## iter 120 value 641.181615
## iter 130 value 630.408704
## iter 140 value 619.613237
## iter 150 value 611.032133
## iter 160 value 609.037849
## iter 170 value 605.680429
## iter 180 value 600.468600
## iter 190 value 592.089937
## iter 200 value 578.679366
## iter 210 value 570.460557
## iter 220 value 561.256566
## iter 230 value 550.447676
## iter 240 value 545.834065
## iter 250 value 542.819047
## iter 260 value 541.366544
## iter 270 value 540.492214
## iter 280 value 540.215353
## iter 290 value 539.994488
## iter 300 value 539.949136
## iter 310 value 539.901212
## iter 320 value 539.839825
## iter 330 value 539.721864
## iter 340 value 539.328052
## iter 350 value 539.138437
## iter 360 value 538.758330
## iter 370 value 538.473643
## iter 380 value 538.199832
## iter 390 value 537.976579
## iter 400 value 537.780806
## iter 410 value 537.645212
## iter 420 value 537.530034
## iter 430 value 537.438562
## iter 440 value 537.436739
## iter 450 value 537.406417
## iter 460 value 537.336946
## iter 470 value 537.242874
## iter 480 value 537.101962
## iter 490 value 536.937019
## iter 500 value 536.788472
## final value 536.788472
## stopped after 500 iterations
## # weights: 106
## initial value 1460565.877414
## iter 10 value 2604.696195
## iter 20 value 1231.521063
## iter 30 value 1043.394233
## iter 40 value 948.020052
## iter 50 value 833.118880
## iter 60 value 737.124196
## iter 70 value 691.535713
## iter 80 value 661.927489
## iter 90 value 621.981945
## iter 100 value 592.558131
## iter 110 value 560.419370
## iter 120 value 535.872650
## iter 130 value 512.997449
## iter 140 value 504.306197
## iter 150 value 496.839702
## iter 160 value 487.764107
## iter 170 value 481.545347
## iter 180 value 476.667601
## iter 190 value 473.778281
## iter 200 value 471.991909
## iter 210 value 468.848764
## iter 220 value 467.471528
## iter 230 value 466.745202
## iter 240 value 464.394145
## iter 250 value 461.843272
## iter 260 value 454.347278
## iter 270 value 450.364408
## iter 280 value 447.799654
## iter 290 value 445.246241
## iter 300 value 441.806379
## iter 310 value 440.266362
## iter 320 value 437.451780
## iter 330 value 431.787413
## iter 340 value 422.709456
## iter 350 value 414.622455
## iter 360 value 408.971817
## iter 370 value 404.770230
## iter 380 value 402.919852
## iter 390 value 401.756895
## iter 400 value 400.851514
## iter 410 value 399.982132
## iter 420 value 399.406058
## iter 430 value 398.852108
## iter 440 value 398.713542
## iter 450 value 398.434726
## iter 460 value 398.097170
## iter 470 value 397.907875
## iter 480 value 397.747425
## iter 490 value 397.677596
## iter 500 value 397.622073
## final value 397.622073
## stopped after 500 iterations
## # weights: 141
## initial value 1390515.760772
## iter 10 value 1573.988546
## iter 20 value 1109.118264
## iter 30 value 964.094875
## iter 40 value 858.884226
## iter 50 value 771.939768
## iter 60 value 719.022903
## iter 70 value 645.597443
## iter 80 value 576.288972
## iter 90 value 540.378345
## iter 100 value 499.671779
## iter 110 value 474.404980
## iter 120 value 451.229096
## iter 130 value 430.808501
## iter 140 value 409.024914
## iter 150 value 393.055717
## iter 160 value 377.187053
## iter 170 value 361.390700
## iter 180 value 353.273033
## iter 190 value 346.860592
## iter 200 value 338.202899
## iter 210 value 328.800874
## iter 220 value 322.312555
## iter 230 value 315.232826
## iter 240 value 306.416530
## iter 250 value 298.244246
## iter 260 value 291.495383
## iter 270 value 282.160253
## iter 280 value 263.279304
## iter 290 value 257.664835
## iter 300 value 254.656368
## iter 310 value 248.739981
## iter 320 value 244.706123
## iter 330 value 239.372479
## iter 340 value 235.700617
## iter 350 value 230.732967
## iter 360 value 223.326551
## iter 370 value 216.920794
## iter 380 value 212.856378
## iter 390 value 206.050621
## iter 400 value 197.978055
## iter 410 value 194.327983
## iter 420 value 192.579616
## iter 430 value 190.283316
## iter 440 value 188.152264
## iter 450 value 186.594779
## iter 460 value 184.864735
## iter 470 value 183.956478
## iter 480 value 183.442826
## iter 490 value 182.820518
## iter 500 value 182.093269
## final value 182.093269
## stopped after 500 iterations
## # weights: 15
## initial value 1527080.175508
## iter 10 value 102805.349590
## iter 20 value 18306.866180
## iter 30 value 11220.529339
## iter 40 value 9758.335008
## iter 50 value 5584.599486
## iter 60 value 2593.036654
## iter 70 value 2314.360343
## iter 80 value 2074.127736
## iter 90 value 1707.529792
## iter 100 value 1632.459034
## iter 110 value 1620.942217
## iter 120 value 1601.733505
## iter 130 value 1600.435375
## iter 140 value 1600.420419
## final value 1600.408066
## converged##################################
# Reporting the cross-validation results
# for the NN model
##################################
NN_Tune## Neural Network
##
## 294 samples
## 5 predictor
##
## Pre-processing: centered (4), scaled (4), ignore (1)
## Resampling: Cross-Validated (10 fold)
## Summary of sample sizes: 266, 264, 265, 264, 265, 266, ...
## Resampling results across tuning parameters:
##
## size decay RMSE Rsquared MAE
## 2 0e+00 3.459517 0.8369414 2.761631
## 2 1e-05 2.493429 0.9074457 1.926148
## 2 1e-04 2.477974 0.9067875 1.874481
## 2 1e-03 2.426691 0.9115333 1.809876
## 2 1e-01 2.353487 0.9162047 1.783663
## 5 0e+00 3.983000 0.7736723 2.182406
## 5 1e-05 2.545260 0.9014702 1.936831
## 5 1e-04 2.917347 0.8648001 1.990064
## 5 1e-03 2.579639 0.8959853 1.848074
## 5 1e-01 2.560729 0.9044140 1.805113
## 10 0e+00 2.941692 0.8734155 2.195624
## 10 1e-05 5.013236 0.7454131 2.569115
## 10 1e-04 4.104210 0.7609624 2.385276
## 10 1e-03 3.199424 0.8511775 2.303877
## 10 1e-01 2.831829 0.8769466 2.114228
## 15 0e+00 4.024030 0.7486857 2.691372
## 15 1e-05 4.260785 0.7695307 2.715268
## 15 1e-04 3.107588 0.8600351 2.333819
## 15 1e-03 3.461486 0.8201023 2.585248
## 15 1e-01 2.914087 0.8805032 2.135890
## 20 0e+00 4.050885 0.7894326 2.908419
## 20 1e-05 4.135612 0.7632034 2.943303
## 20 1e-04 3.706096 0.8182675 2.825326
## 20 1e-03 4.300572 0.7507562 3.033962
## 20 1e-01 3.155988 0.8634645 2.332871
##
## RMSE was used to select the optimal model using the smallest value.
## The final values used for the model were size = 2 and decay = 0.1.NN_Tune$finalModel## a 5-2-1 network with 15 weights
## inputs: GENDERFemale INFMOR PERCAP CLTECH NCOMOR
## output(s): .outcome
## options were - linear output units decay=0.1(NN_Tune_RMSE <- NN_Tune$results[NN_Tune$results$size==NN_Tune$bestTune$size &
NN_Tune$results$decay==NN_Tune$bestTune$decay,
c("RMSE")])## [1] 2.353487(NN_Tune_Rsquared <- NN_Tune$results[NN_Tune$results$size==NN_Tune$bestTune$size &
NN_Tune$results$decay==NN_Tune$bestTune$decay,
c("Rsquared")])## [1] 0.9162047(NN_Tune_MAE <- NN_Tune$results[NN_Tune$results$size==NN_Tune$bestTune$size &
NN_Tune$results$decay==NN_Tune$bestTune$decay,
c("MAE")])## [1] 1.783663##################################
# Identifying and plotting the
# best model predictors
# for the NN model
##################################
NN_VarImp <- varImp(NN_Tune, scale = TRUE)
plot(NN_VarImp,
scales=list(y=list(cex = .95)),
main="Ranked Variable Importance : NN",
xlab="Scaled Variable Importance Metrics",
ylab="Predictors",
cex=2,
origin=0,
alpha=0.45)
1.3.6.4 Partial Least Squares Regression (PLS)
Code Chunk | Output
##################################
# Defining the model hyperparameter values
# for the PLS model
##################################
PLS_Grid = expand.grid(ncomp = 1:5)
##################################
# Running the PLS model
# by setting the caret method to 'pls'
##################################
set.seed(12345678)
PLS_Tune <- train(x = MD.Model.Predictors,
y = MD$LIFEXP,
method = "pls",
tuneGrid = PLS_Grid,
trControl = KFold_Control)
##################################
# Reporting the cross-validation results
# for the PLS model
##################################
PLS_Tune## Partial Least Squares
##
## 294 samples
## 5 predictor
##
## No pre-processing
## Resampling: Cross-Validated (10 fold)
## Summary of sample sizes: 266, 264, 265, 264, 265, 266, ...
## Resampling results across tuning parameters:
##
## ncomp RMSE Rsquared MAE
## 1 5.107641 0.5809693 4.152802
## 2 3.229570 0.8398823 2.428972
## 3 2.819900 0.8781609 2.095819
## 4 2.769699 0.8837208 2.114378
## 5 2.768609 0.8838715 2.110610
##
## RMSE was used to select the optimal model using the smallest value.
## The final value used for the model was ncomp = 5.PLS_Tune$finalModel## Partial least squares regression , fitted with the orthogonal scores algorithm.
## Call:
## plsr(formula = .outcome ~ ., ncomp = ncomp, data = dat, method = "oscorespls")(PLS_Tune_RMSE <- PLS_Tune$results[PLS_Tune$results$ncomp==PLS_Tune$bestTune$ncomp,
c("RMSE")])## [1] 2.768609(PLS_Tune_Rsquared <- PLS_Tune$results[PLS_Tune$results$ncomp==PLS_Tune$bestTune$ncomp,
c("Rsquared")])## [1] 0.8838715(PLS_Tune_MAE <- PLS_Tune$results[PLS_Tune$results$ncomp==PLS_Tune$bestTune$ncomp,
c("MAE")])## [1] 2.11061##################################
# Identifying and plotting the
# best model predictors
# for the PLS model
##################################
PLS_VarImp <- varImp(PLS_Tune, scale = TRUE)
plot(PLS_VarImp,
scales=list(y=list(cex = .95)),
main="Ranked Variable Importance : PLS",
xlab="Scaled Variable Importance Metrics",
ylab="Predictors",
cex=2,
origin=0,
alpha=0.45)
1.3.6.5 Cubist Regression (CUBIST)
Code Chunk | Output
##################################
# Defining the model hyperparameter values
# for the CUBIST model
##################################
CUBIST_Grid = expand.grid(committees = c(10, 20, 30, 40, 50),
neighbors = c(0, 3, 6, 9))
##################################
# Running the CUBIST model
# by setting the caret method to 'cubist'
##################################
set.seed(12345678)
CUBIST_Tune <- train(x = MD.Model.Predictors,
y = MD$LIFEXP,
method = "cubist",
tuneGrid = CUBIST_Grid,
trControl = KFold_Control)
##################################
# Reporting the cross-validation results
# for the CUBIST model
##################################
CUBIST_Tune## Cubist
##
## 294 samples
## 5 predictor
##
## No pre-processing
## Resampling: Cross-Validated (10 fold)
## Summary of sample sizes: 266, 264, 265, 264, 265, 266, ...
## Resampling results across tuning parameters:
##
## committees neighbors RMSE Rsquared MAE
## 10 0 2.349910 0.9176282 1.743119
## 10 3 2.463103 0.9118159 1.826258
## 10 6 2.414374 0.9147585 1.779205
## 10 9 2.371853 0.9172327 1.751077
## 20 0 2.325278 0.9194823 1.729501
## 20 3 2.452245 0.9128512 1.817801
## 20 6 2.403467 0.9158193 1.775160
## 20 9 2.365302 0.9179693 1.750785
## 30 0 2.288992 0.9216589 1.703604
## 30 3 2.441377 0.9135986 1.809325
## 30 6 2.385727 0.9169876 1.763454
## 30 9 2.346537 0.9191819 1.738438
## 40 0 2.288357 0.9211136 1.706122
## 40 3 2.436769 0.9136943 1.811555
## 40 6 2.383490 0.9168822 1.766773
## 40 9 2.345004 0.9190023 1.743124
## 50 0 2.283377 0.9214369 1.704048
## 50 3 2.434991 0.9139000 1.815273
## 50 6 2.382253 0.9170399 1.769362
## 50 9 2.343006 0.9192188 1.745291
##
## RMSE was used to select the optimal model using the smallest value.
## The final values used for the model were committees = 50 and neighbors = 0.CUBIST_Tune$finalModel##
## Call:
## cubist.default(x = x, y = y, committees = param$committees)
##
## Number of samples: 294
## Number of predictors: 5
##
## Number of committees: 50
## Number of rules per committee: 6, 2, 4, 4, 5, 2, 4, 3, 4, 2, 4, 2, 3, 2, 4, 2, 3, 2, 4, 2 ...(CUBIST_Tune_RMSE <- CUBIST_Tune$results[CUBIST_Tune$results$committees==CUBIST_Tune$bestTune$committees &
CUBIST_Tune$results$neighbors==CUBIST_Tune$bestTune$neighbors,
c("RMSE")])## [1] 2.283377(CUBIST_Tune_Rsquared <- CUBIST_Tune$results[CUBIST_Tune$results$committees==CUBIST_Tune$bestTune$committees &
CUBIST_Tune$results$neighbors==CUBIST_Tune$bestTune$neighbors,
c("Rsquared")])## [1] 0.9214369(CUBIST_Tune_MAE <- CUBIST_Tune$results[CUBIST_Tune$results$committees==CUBIST_Tune$bestTune$committees &
CUBIST_Tune$results$neighbors==CUBIST_Tune$bestTune$neighbors,
c("MAE")])## [1] 1.704048##################################
# Identifying and plotting the
# best model predictors
# for the CUBIST model
##################################
CUBIST_VarImp <- varImp(CUBIST_Tune, scale = TRUE)
plot(CUBIST_VarImp,
scales=list(y=list(cex = .95)),
main="Ranked Variable Importance : CUBIST",
xlab="Scaled Variable Importance Metrics",
ylab="Predictors",
cex=2,
origin=0,
alpha=0.45)
1.3.7 Model Performance Validation
Code Chunk | Output
##################################
# Evaluating the models
# on the model test data
##################################
##################################
# Formulating the DALEX object
# for the Best GBM model
##################################
GBM_DALEX <- DALEX::explain(GBM_Tune,
data = MT.Model.Predictors,
y = MT$LIFEXP,
verbose = FALSE,
label = "GBM")
(GBM_DALEX_Performance <- model_performance(GBM_DALEX))## Measures for: regression
## mse : 3.493083
## rmse : 1.868979
## r2 : 0.9362058
## mad : 0.9972678
##
## Residuals:
## 0% 10% 20% 30% 40% 50%
## -4.20752484 -2.15021430 -1.48672408 -0.87866008 -0.39263579 -0.01705431
## 60% 70% 80% 90% 100%
## 0.35387527 0.67211084 1.30280583 2.02235201 6.02771793(GBM_DALEX_Diagnostics <- model_diagnostics(GBM_DALEX))## GENDER INFMOR PERCAP CLTECH
## Male :43 Min. :0.5306 Min. :-1.4775 Min. : 0.20
## Female:29 1st Qu.:1.5623 1st Qu.: 0.8076 1st Qu.: 31.15
## Median :2.6602 Median : 1.5798 Median : 83.50
## Mean :2.5206 Mean : 1.7061 Mean : 67.12
## 3rd Qu.:3.4491 3rd Qu.: 2.8175 3rd Qu.:100.00
## Max. :4.4864 Max. : 4.2333 Max. :100.00
## NCOMOR y y_hat residuals
## Min. :2.511 Min. :53.79 Min. :56.81 Min. :-4.20753
## 1st Qu.:3.887 1st Qu.:67.49 1st Qu.:67.37 1st Qu.:-1.04218
## Median :4.685 Median :73.35 Median :72.36 Median :-0.01705
## Mean :4.736 Mean :72.42 Mean :72.48 Mean :-0.06016
## 3rd Qu.:5.631 3rd Qu.:78.39 3rd Qu.:78.86 3rd Qu.: 0.87535
## Max. :7.579 Max. :86.20 Max. :84.55 Max. : 6.02772
## abs_residuals label ids
## Min. :0.06685 Length:72 Min. : 1.00
## 1st Qu.:0.46126 Class :character 1st Qu.:18.75
## Median :0.99727 Mode :character Median :36.50
## Mean :1.40941 Mean :36.50
## 3rd Qu.:1.99579 3rd Qu.:54.25
## Max. :6.02772 Max. :72.00plot(GBM_DALEX_Diagnostics,
variable = "y",
yvariable = "y_hat") +
geom_point(size=3) +
scale_x_continuous("Observed LIFEXP") +
scale_y_continuous("Predicted LIFEXP") +
geom_abline(slope = 1) +
ggtitle("GBM: Observed and Predicted LIFEXP")
##################################
# Formulating the DALEX object
# for the Best RF model
##################################
RF_DALEX <- DALEX::explain(RF_Tune,
data = MT.Model.Predictors,
y = MT$LIFEXP,
verbose = FALSE,
label = "RF")
(RF_DALEX_Performance <- model_performance(RF_DALEX))## Measures for: regression
## mse : 4.504021
## rmse : 2.122268
## r2 : 0.917743
## mad : 1.284074
##
## Residuals:
## 0% 10% 20% 30% 40% 50%
## -5.22877943 -2.17233443 -1.33595313 -0.66251307 -0.31119611 0.01921082
## 60% 70% 80% 90% 100%
## 0.26151554 0.80194556 1.84018856 2.72166332 6.62793907(RF_DALEX_Diagnostics <- model_diagnostics(RF_DALEX))## GENDER INFMOR PERCAP CLTECH
## Male :43 Min. :0.5306 Min. :-1.4775 Min. : 0.20
## Female:29 1st Qu.:1.5623 1st Qu.: 0.8076 1st Qu.: 31.15
## Median :2.6602 Median : 1.5798 Median : 83.50
## Mean :2.5206 Mean : 1.7061 Mean : 67.12
## 3rd Qu.:3.4491 3rd Qu.: 2.8175 3rd Qu.:100.00
## Max. :4.4864 Max. : 4.2333 Max. :100.00
## NCOMOR y y_hat residuals
## Min. :2.511 Min. :53.79 Min. :54.37 Min. :-5.22878
## 1st Qu.:3.887 1st Qu.:67.49 1st Qu.:68.13 1st Qu.:-1.20707
## Median :4.685 Median :73.35 Median :72.39 Median : 0.01921
## Mean :4.736 Mean :72.42 Mean :72.31 Mean : 0.11035
## 3rd Qu.:5.631 3rd Qu.:78.39 3rd Qu.:78.82 3rd Qu.: 1.46992
## Max. :7.579 Max. :86.20 Max. :84.60 Max. : 6.62794
## abs_residuals label ids
## Min. :0.03082 Length:72 Min. : 1.00
## 1st Qu.:0.46750 Class :character 1st Qu.:18.75
## Median :1.28407 Mode :character Median :36.50
## Mean :1.56501 Mean :36.50
## 3rd Qu.:2.23392 3rd Qu.:54.25
## Max. :6.62794 Max. :72.00plot(RF_DALEX_Diagnostics,
variable = "y",
yvariable = "y_hat") +
geom_point(size=3) +
scale_x_continuous("Observed LIFEXP") +
scale_y_continuous("Predicted LIFEXP") +
geom_abline(slope = 1) +
ggtitle("RF: Observed and Predicted LIFEXP")
##################################
# Formulating the DALEX object
# for the Best NN model
##################################
NN_DALEX <- DALEX::explain(NN_Tune,
data = MT.Model.Predictors,
y = MT$LIFEXP,
verbose = FALSE,
label = "NN")
(NN_DALEX_Performance <- model_performance(NN_DALEX))## Measures for: regression
## mse : 3.981273
## rmse : 1.995313
## r2 : 0.92729
## mad : 1.331898
##
## Residuals:
## 0% 10% 20% 30% 40% 50% 60%
## -5.0566398 -1.9253734 -1.4168918 -1.1691032 -0.3808666 0.2508203 0.3866501
## 70% 80% 90% 100%
## 1.0424427 1.7914332 2.9384548 4.8353156(NN_DALEX_Diagnostics <- model_diagnostics(NN_DALEX))## GENDER INFMOR PERCAP CLTECH
## Male :43 Min. :0.5306 Min. :-1.4775 Min. : 0.20
## Female:29 1st Qu.:1.5623 1st Qu.: 0.8076 1st Qu.: 31.15
## Median :2.6602 Median : 1.5798 Median : 83.50
## Mean :2.5206 Mean : 1.7061 Mean : 67.12
## 3rd Qu.:3.4491 3rd Qu.: 2.8175 3rd Qu.:100.00
## Max. :4.4864 Max. : 4.2333 Max. :100.00
## NCOMOR y y_hat residuals
## Min. :2.511 Min. :53.79 Min. :53.77 Min. :-5.0566
## 1st Qu.:3.887 1st Qu.:67.49 1st Qu.:67.91 1st Qu.:-1.3453
## Median :4.685 Median :73.35 Median :72.39 Median : 0.2508
## Mean :4.736 Mean :72.42 Mean :72.30 Mean : 0.1240
## 3rd Qu.:5.631 3rd Qu.:78.39 3rd Qu.:78.90 3rd Qu.: 1.2255
## Max. :7.579 Max. :86.20 Max. :84.31 Max. : 4.8353
## abs_residuals label ids
## Min. :0.08507 Length:72 Min. : 1.00
## 1st Qu.:0.68395 Class :character 1st Qu.:18.75
## Median :1.33190 Mode :character Median :36.50
## Mean :1.58876 Mean :36.50
## 3rd Qu.:2.14093 3rd Qu.:54.25
## Max. :5.05664 Max. :72.00plot(NN_DALEX_Diagnostics,
variable = "y",
yvariable = "y_hat") +
geom_point(size=3) +
scale_x_continuous("Observed LIFEXP") +
scale_y_continuous("Predicted LIFEXP") +
geom_abline(slope = 1) +
ggtitle("NN: Observed and Predicted LIFEXP")
##################################
# Formulating the DALEX object
# for the Best PLS model
##################################
PLS_DALEX <- DALEX::explain(PLS_Tune,
data = MT.Model.Predictors,
y = MT$LIFEXP,
verbose = FALSE,
label = "PLS")
(PLS_DALEX_Performance <- model_performance(PLS_DALEX))## Measures for: regression
## mse : 8.772658
## rmse : 2.961867
## r2 : 0.8397849
## mad : 1.687233
##
## Residuals:
## 0% 10% 20% 30% 40% 50% 60%
## -8.2953487 -3.3619601 -2.3734314 -1.1325844 -0.4814388 0.1923809 0.7761085
## 70% 80% 90% 100%
## 1.3418691 2.3002494 4.1779864 6.2664318(PLS_DALEX_Diagnostics <- model_diagnostics(PLS_DALEX))## GENDER INFMOR PERCAP CLTECH
## Male :43 Min. :0.5306 Min. :-1.4775 Min. : 0.20
## Female:29 1st Qu.:1.5623 1st Qu.: 0.8076 1st Qu.: 31.15
## Median :2.6602 Median : 1.5798 Median : 83.50
## Mean :2.5206 Mean : 1.7061 Mean : 67.12
## 3rd Qu.:3.4491 3rd Qu.: 2.8175 3rd Qu.:100.00
## Max. :4.4864 Max. : 4.2333 Max. :100.00
## NCOMOR y y_hat residuals
## Min. :2.511 Min. :53.79 Min. :58.14 Min. :-8.29535
## 1st Qu.:3.887 1st Qu.:67.49 1st Qu.:67.39 1st Qu.:-1.73571
## Median :4.685 Median :73.35 Median :72.32 Median : 0.19238
## Mean :4.736 Mean :72.42 Mean :72.34 Mean : 0.07797
## 3rd Qu.:5.631 3rd Qu.:78.39 3rd Qu.:78.60 3rd Qu.: 1.59971
## Max. :7.579 Max. :86.20 Max. :86.20 Max. : 6.26643
## abs_residuals label ids
## Min. :0.01028 Length:72 Min. : 1.00
## 1st Qu.:0.77705 Class :character 1st Qu.:18.75
## Median :1.68723 Mode :character Median :36.50
## Mean :2.27256 Mean :36.50
## 3rd Qu.:3.40953 3rd Qu.:54.25
## Max. :8.29535 Max. :72.00plot(PLS_DALEX_Diagnostics,
variable = "y",
yvariable = "y_hat") +
geom_point(size=3) +
scale_x_continuous("Observed LIFEXP") +
scale_y_continuous("Predicted LIFEXP") +
geom_abline(slope = 1) +
ggtitle("PLS: Observed and Predicted LIFEXP")
##################################
# Formulating the DALEX object
# for the Best CUBIST model
##################################
CUBIST_DALEX <- DALEX::explain(CUBIST_Tune,
data = MT.Model.Predictors,
y = MT$LIFEXP,
verbose = FALSE,
label = "CUBIST")
(CUBIST_DALEX_Performance <- model_performance(CUBIST_DALEX))## Measures for: regression
## mse : 4.252961
## rmse : 2.062271
## r2 : 0.9223281
## mad : 1.504065
##
## Residuals:
## 0% 10% 20% 30% 40% 50%
## -4.24762244 -2.06826235 -1.58225898 -1.15000147 -0.43114893 0.03237576
## 60% 70% 80% 90% 100%
## 0.34857635 1.11785478 1.58806246 2.95097520 6.41232629(CUBIST_DALEX_Diagnostics <- model_diagnostics(CUBIST_DALEX))## GENDER INFMOR PERCAP CLTECH
## Male :43 Min. :0.5306 Min. :-1.4775 Min. : 0.20
## Female:29 1st Qu.:1.5623 1st Qu.: 0.8076 1st Qu.: 31.15
## Median :2.6602 Median : 1.5798 Median : 83.50
## Mean :2.5206 Mean : 1.7061 Mean : 67.12
## 3rd Qu.:3.4491 3rd Qu.: 2.8175 3rd Qu.:100.00
## Max. :4.4864 Max. : 4.2333 Max. :100.00
## NCOMOR y y_hat residuals
## Min. :2.511 Min. :53.79 Min. :55.96 Min. :-4.24762
## 1st Qu.:3.887 1st Qu.:67.49 1st Qu.:67.43 1st Qu.:-1.51031
## Median :4.685 Median :73.35 Median :72.64 Median : 0.03238
## Mean :4.736 Mean :72.42 Mean :72.32 Mean : 0.10287
## 3rd Qu.:5.631 3rd Qu.:78.39 3rd Qu.:78.58 3rd Qu.: 1.29561
## Max. :7.579 Max. :86.20 Max. :84.74 Max. : 6.41233
## abs_residuals label ids
## Min. :0.009592 Length:72 Min. : 1.00
## 1st Qu.:0.499008 Class :character 1st Qu.:18.75
## Median :1.504065 Mode :character Median :36.50
## Mean :1.591015 Mean :36.50
## 3rd Qu.:1.926649 3rd Qu.:54.25
## Max. :6.412326 Max. :72.00plot(CUBIST_DALEX_Diagnostics,
variable = "y",
yvariable = "y_hat") +
geom_point(size=3) +
scale_x_continuous("Observed LIFEXP") +
scale_y_continuous("Predicted LIFEXP") +
geom_abline(slope = 1) +
ggtitle("CUBIST: Observed and Predicted LIFEXP")
1.3.8 Model Selection
Code Chunk | Output
##################################
# Consolidating the performance
# on the model test data
##################################
plot(GBM_DALEX_Performance,
RF_DALEX_Performance,
NN_DALEX_Performance,
PLS_DALEX_Performance,
CUBIST_DALEX_Performance)
plot(GBM_DALEX_Performance,
RF_DALEX_Performance,
NN_DALEX_Performance,
PLS_DALEX_Performance,
CUBIST_DALEX_Performance,
geom = "boxplot")
plot(GBM_DALEX_Performance,
RF_DALEX_Performance,
NN_DALEX_Performance,
PLS_DALEX_Performance,
CUBIST_DALEX_Performance,
geom = "histogram")
##################################
# Consolidating the variable importance
# on the model test data
##################################
GBM_DALEX_VariableImportance <- model_parts(GBM_DALEX,
loss_function = loss_root_mean_square,
B = 200,
N = NULL)
RF_DALEX_VariableImportance <- model_parts(RF_DALEX,
loss_function = loss_root_mean_square,
B = 200,
N = NULL)
NN_DALEX_VariableImportance <- model_parts(NN_DALEX,
loss_function = loss_root_mean_square,
B = 200,
N = NULL)
PLS_DALEX_VariableImportance <- model_parts(PLS_DALEX,
loss_function = loss_root_mean_square,
B = 200,
N = NULL)
CUBIST_DALEX_VariableImportance <- model_parts(CUBIST_DALEX,
loss_function = loss_root_mean_square,
B = 200,
N = NULL)
plot(GBM_DALEX_VariableImportance,
RF_DALEX_VariableImportance,
NN_DALEX_VariableImportance,
PLS_DALEX_VariableImportance,
CUBIST_DALEX_VariableImportance)
1.3.9 Model Post-Hoc Analysis
Code Chunk | Output
##################################
# Summarizing the variable importance
# for the final model - GBM
##################################
GBM_DALEX_VariableImportance## variable mean_dropout_loss label
## 1 _full_model_ 1.868979 GBM
## 2 PERCAP 2.088901 GBM
## 3 GENDER 2.237822 GBM
## 4 CLTECH 2.334086 GBM
## 5 NCOMOR 4.002445 GBM
## 6 INFMOR 7.689843 GBM
## 7 _baseline_ 10.077581 GBMplot(GBM_DALEX_VariableImportance)
##################################
# Formulating the partial dependence plots
# for the final model - GBM
# using the different variables
##################################
GBM_DALEX_PartialDependencePlot_INFMOR <- model_profile(GBM_DALEX,
variables = "INFMOR")
GBM_DALEX_PartialDependencePlot_NCOMOR <- model_profile(GBM_DALEX,
variables = "NCOMOR")
GBM_DALEX_PartialDependencePlot_CLTECH <- model_profile(GBM_DALEX,
variables = "CLTECH")
GBM_DALEX_PartialDependencePlot_PERCAP <- model_profile(GBM_DALEX,
variables = "PERCAP")
(GBM_DALEX_PDP_INFMOR <- plot(GBM_DALEX_PartialDependencePlot_INFMOR,
geom = "profiles"))
(GBM_DALEX_PDP_NCOMOR <- plot(GBM_DALEX_PartialDependencePlot_NCOMOR,
geom = "profiles"))
(GBM_DALEX_PDP_CLTECH <- plot(GBM_DALEX_PartialDependencePlot_CLTECH,
geom = "profiles"))
(GBM_DALEX_PDP_PERCAP <- plot(GBM_DALEX_PartialDependencePlot_PERCAP,
geom = "profiles"))
##################################
# Formulating the grouped partial dependence plots
# for the final model - GBM
# using the different variables
##################################
GBM_DALEX_GroupedPartialDependencePlot_INFMOR <- model_profile(GBM_DALEX,
variables = "INFMOR",
groups = "GENDER")
GBM_DALEX_GroupedPartialDependencePlot_NCOMOR <- model_profile(GBM_DALEX,
variables = "NCOMOR",
groups = "GENDER")
GBM_DALEX_GroupedPartialDependencePlot_CLTECH <- model_profile(GBM_DALEX,
variables = "CLTECH",
groups = "GENDER")
GBM_DALEX_GroupedPartialDependencePlot_PERCAP <- model_profile(GBM_DALEX,
variables = "PERCAP",
groups = "GENDER")
(GBM_DALEX_GPDP_INFMOR <- plot(GBM_DALEX_GroupedPartialDependencePlot_INFMOR,
geom = "profiles"))
(GBM_DALEX_GPDP_NCOMOR <- plot(GBM_DALEX_GroupedPartialDependencePlot_NCOMOR,
geom = "profiles"))
(GBM_DALEX_GPDP_CLTECH <- plot(GBM_DALEX_GroupedPartialDependencePlot_CLTECH,
geom = "profiles"))
(GBM_DALEX_GPDP_PERCAP <- plot(GBM_DALEX_GroupedPartialDependencePlot_PERCAP,
geom = "profiles"))
1.4 Summary
2. References
[Book] Explanatory Model Analysis: Explore, Explain, and Examine Predictive Models With examples in R and Python by Przemyslaw Biecek and Tomasz Burzykowski
[Book] Explainable Machine Learning: A Guide for Making Black Box Models Explainable by Christoph Molnar
[Book] Explainable AI: Interpreting, Explaining and Visualizing Deep Learning by Wojciech Samek, Gregoire Montavon, Andrea Vedaldi, Lars Kai Hansen and Klaus-Robert Muller
[R Package] DALEX by Przemyslaw Biecek, Szymon Maksymiuk and Hubert Baniecki
[R Package] iml by Christoph Molnar
[R Package] ALEPlot by Dan Apley
[R Package] randomForest by Leo Breiman, Adele Cutler, Andy Liaw and Matthew Wiener
[R Package] auditor by Alicja Gosiewska, Przemyslaw Biecek, Hubert Baniecki and Tomasz Mikołajczyk
[R Package] fastshap by Brandon Greenwell
[R Package] rms by Frank Harrell
[R Package] EIX by Szymon Maksymiuk, Ewelina Karbowiak and Przemyslaw Biecek
[R Package] parsnip by Max Kuhn and Davis Vaughan
[R Package] h2o by Tomas Fryda, Erin LeDell, Navdeep Gill, Spencer Aiello, Anqi Fu, Arno Candel, Cliff Click, Tom Kraljevic, Tomas Nykodym, Patrick Aboyoun, Michal Kurka, Michal Malohlava, Sebastien Poirier and Wendy Wong
[R Package] tidymodels by Max Kuhn and Hadley Wickham
[R Package] e1071 by David Meyer, Evgenia Dimitriadou, Kurt Hornik, Andreas Weingessel and Friedrich Leisch
[R Package] lime by Emil Hvitfeldt, Thomas Lin Pedersen and Michael Benesty
[R Package] ExplainPrediction by Marko Robnik-Sikonja
[R Package] localModel by Przemyslaw Biecek and Mateusz Staniak
[R Package] skimr by Elin Waring
[R Package] corrplot by Taiyun Wei
[R Package] lares by Bernardo Lares
[R Package] minerva by Michele Filosi
[R Package] CORElearn by Marko Robnik-Sikonja and Petr Savicky
[R Package] caret by Max Kuhn
[R Package] gbm by Brandon Greenwell, Bradley Boehmke, Jay Cunningham and GBM Developers
[R Package] randomForest by Andy Liaw
[R Package] nnet by Brian Ripley
[R Package] pls by Kristian Hovde Liland
[R Package] Cubist by Max Kuhn
[R Package] patchwork by Thomas Lin Pedersen
[Article] Interpretation Methods for Black-Box Machine Learning Models in Insurance Rating-Type Applications by Gabe Taylor, Sunish Menon, Huimin Ru, Ray Wright, Xin Hunt and Ralph Abbey
[Article] 4 Model-Agnostic Interpretability Techniques for Complex Models by Funda Gunes
[Article] How Can We Provide Post-Hoc Explanations for Black-Box AI Models? by Joy Lin
[Publication] A Survey of Methods for Explaining Black Box Models by Riccardo Guidotti, Anna Monreale, Salvatore Ruggieri, Franco Turini, Fosca Giannotti and Dino Pedreschi (ACM Computing Surveys)
[Publication] iml: An R package for Interpretable Machine Learning by Christoph Molnar (Journal of Open Source Software)